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Nocardia is an environmental aerobic actinobacterium (Actinomycete) that stains positive on Gram stain and forms commonly in soil and water. Infection is primarily acquired through inhalation; however, it may also [...]

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. These biases have been identified as a major source of uncertainty in carbon cycle projections, hampering predictive capacity. In this study, we open the proverbial Pandora's box and peer under the lid of strategies to tackle climate model ensemble uncertainty. 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 reduce the climate-driven 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. Bias correction can improve model carbon outputs, but 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 simulated carbon over time compared to the target dataset, highlighting the need to account for temporal properties in correction or ensemble-averaging methods. Multivariate bias correction methods tend to reduce the uncertainty more than univariate approaches, although the overall magnitude is similar. Even after correcting the bias in the meteorological forcing dataset, the simulated vegetation distribution presents different patterns when different GCMs are used to drive LPJ-GUESS. Additionally, we found 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. This highlights that, where possible, an arithmetic ensemble average should be avoided. However, potential target datasets that would facilitate the application of machine learning approaches, i.e., that cover both the spatial and temporal domain required to derive a robust informed ensemble average, are sparse for ecosystem variables. [ABSTRACT FROM AUTHOR]

Describes the course unit, "Global Environmental Crises," that encompasses four environmental issues and involves a variety of information technology (IT) based components. Discusses the five rationales for the development of this course, summarizes formative and summative evaluations, and analyses the implications of this unit. (CMK)

Climate projections from global circulation models (GCMs) part of the Coupled Model Intercomparison Project (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 5 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]

Summary: Predicting species‐level responses to drought at the landscape scale is critical to reducing uncertainty in future terrestrial carbon and water cycle projections.We embedded a stomatal optimisation model in the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and parameterised the model for 15 canopy dominant eucalypt tree species across South‐Eastern Australia (mean annual precipitation range: 344–1424 mm yr−1). We conducted three experiments: applying CABLE to the 2017–2019 drought; a 20% drier drought; and a 20% drier drought with a doubling of atmospheric carbon dioxide (CO2).The severity of the drought was highlighted as for at least 25% of their distribution ranges, 60% of species experienced leaf water potentials beyond the water potential at which 50% of hydraulic conductivity is lost due to embolism. We identified areas of severe hydraulic stress within‐species' ranges, but we also pinpointed resilience in species found in predominantly semiarid areas. The importance of the role of CO2 in ameliorating drought stress was consistent across species.Our results represent an important advance in our capacity to forecast the resilience of individual tree species, providing an evidence base for decision‐making around the resilience of restoration plantings or net‐zero emission strategies. [ABSTRACT FROM AUTHOR]

Australia plays an important role in the global terrestrial carbon cycle on inter-annual timescales. While the Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations of net biome production (NBP) and the carbon stored in vegetation between 1901 to 2018 from 13 DGVMs (TRENDY v8 ensemble). We focused our analysis on Australia's short-term (inter-annual) and long-term (decadal to centennial) terrestrial carbon dynamics. The TRENDY models simulated differing magnitudes of NBP on inter-annual timescales, and these differences resulted in significant differences in long-term vegetation carbon accumulation (-4.7 to 9.5 PgC). We compared the TRENDY ensemble to several satellite-derived datasets and showed that the spread in the models' simulated carbon storage resulted from varying changes in carbon residence time rather than differences in net carbon uptake. Differences in simulated long-term accumulated NBP between models were mostly due to model responses to land-use change. The DGVMs also simulated different sensitivities to atmospheric carbon dioxide (CO2) concentration, although notably, the models with nutrient cycles did not simulate the smallest NBP response to CO2. Our results suggest that a change in the climate forcing did not have a large impact on the carbon cycle on long timescales. However, the inter-annual variability in precipitation drives the year-to-year variability in NBP. We analysed the impact of key modes of climate variability, including the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), on NBP. While the DGVMs agreed on sign of the response of NBP to El Niño and La Niña and to positive and negative IOD events, the magnitude of inter-annual variability in NBP differed strongly between models. In addition, we find that differences in the timing of simulated phenology and fire dynamics are associated with differences in simulated or prescribed vegetation cover and process representation. We further find model disagreement in simulated vegetation carbon, phenology, and apparent carbon residence time, indicating that the models have different types and coverage of vegetation across Australia (whether prescribed or emergent). Our study highlights the need to evaluate parameter assumptions and the key processes that drive vegetation dynamics, such as phenology, mortality, and fire, in an Australian context to reduce uncertainty across models. [ABSTRACT FROM AUTHOR]

The El Niño‐-Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) central Pacific (CP) and (ii) eastern Pacific (EP). Both types of El Niño are characterised by above-average sea surface temperature anomalies at the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the amplitude of the concentration of interannual atmospheric carbon dioxide (CO2) variability following increased tropical near-surface air temperature and decreased precipitation. We employ the dynamic global vegetation model LPJ-GUESS (Lund–Potsdam–Jena General Ecosystem Simulator) within a synthetic experimental framework to examine the sensitivity and potential long-term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the latter half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was small for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle. [ABSTRACT FROM AUTHOR]

Compound events have the potential to cause high socioeconomic and environmental losses. We examine the ability of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models to capture two bivariate compound events: the co‐occurrence of heavy rain and strong wind, and heat waves and meteorological drought. We evaluate the models over North America, Europe, Eurasia, and Australia using observations and reanalysis data set spanning 1980–2014. Some of the CMIP6 models capture the return periods of both bivariate compound events over North America, Europe, and Eurasia surprisingly well but perform less well over Australia. For heavy rain and strong wind, this poor performance was particularly clear in northern Australia which suggests limits in simulating tropical and extratropical cyclones, local convection, and mesoscale convective systems. We did not find higher model resolution improved performance in any region. Overall, our results show some CMIP6 models can be used to examine compound events, particularly over North America, Europe, and Eurasia. Plain Language Summary: Compound events, such as the co‐occurrence of heavy rain and strong wind or heat waves and drought, can have major economic, social, and environmental consequences. We therefore ask the question whether the new generation of climate models represented by the sixth phase of the Coupled Model Intercomparison Project (CMIP6) can simulate the occurrence of these important events. We found that some of the CMIP6 models do simulate these compound events surprisingly well over North America, Europe, and Eurasia. Unfortunately, they perform less well over Australia which is likely associated with the problem of simulating extratropical cyclones, local convection, and mesoscale convective systems. Our results suggest that some CMIP6 models can be used to examine these two compound events particularly over North America, Europe, and Eurasia. Key Points: Some CMIP6 models reproduce observed return periods of co‐occurring rain and wind extremes and co‐occurring heat waves and droughts wellCMIP6 models simulate these compound events over North America, Europe, or Eurasia with similar levels of skillCMIP6 models simulate these compound events over Australia with lower skill than the other regions analyzed [ABSTRACT FROM AUTHOR]

South‐East Australia has recently been subjected to two of the worst droughts in the historical record (Millennium Drought, 2000–2009 and Big Dry, 2017–2019). Unfortunately, a lack of forest monitoring has made it difficult to determine whether widespread tree mortality has resulted from these droughts. Anecdotal observations suggest the Big Dry may have led to more significant tree mortality than the Millennium drought. Critically, to be able to robustly project future expected climate change effects on Australian vegetation, we need to assess the vulnerability of Australian trees to drought. Here we implemented a model of plant hydraulics into the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. We parameterized the drought response behaviour of five broad vegetation types, based on a common garden dry‐down experiment with species originating across a rainfall gradient (188–1,125 mm/year) across South‐East Australia. The new hydraulics model significantly improved (~35%–45% reduction in root mean square error) CABLE's previous predictions of latent heat fluxes during periods of water stress at two eddy covariance sites in Australia. Landscape‐scale predictions of the greatest percentage loss of hydraulic conductivity (PLC) of about 40%–60%, were broadly consistent with satellite estimates of regions of the greatest change in both droughts. In neither drought did CABLE predict that trees would have reached critical PLC in widespread areas (i.e. it projected a low mortality risk), although the model highlighted critical levels near the desert regions of South‐East Australia where few trees live. Overall, our experimentally constrained model results imply significant resilience to drought conferred by hydraulic function, but also highlight critical data and scientific gaps. Our approach presents a promising avenue to integrate experimental data and make regional‐scale predictions of potential drought‐induced hydraulic failure. [ABSTRACT FROM AUTHOR]

The El Niño-Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) Central-Pacific (CP) and (ii) Eastern-Pacific (EP). Both types of El Nino are characterised by above average sea surface temperature anomalies in the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the amplitude of the concentration of interannual atmospheric carbon dioxide (CO2) variability, as well as different lags in terrestrial CO2 release to the atmosphere following increased tropical near surface air temperature. We employ the dynamic global vegetation model LPJ-GUESS within a synthetic experimental framework to examine the sensitivity and potential long term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the later half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was negligible for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle. [ABSTRACT FROM AUTHOR]

We quantify the skill of Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models to represent daily temperature extremes. We find CMIP models systematically exaggerate the magnitude of daily temperature anomalies for both cold and hot extremes. We assess the contribution to a daily temperature extreme from four terms: the long‐term mean annual cycle, the diurnal cycle, synoptic variability, and seasonal variability for both cold and hot extremes. These four terms are combined, and the overall performance of individual climate models assessed. This identifies those models that can simulate temperature extremes well and simulate them well for the right reasons. The new error metric shows that increases in horizontal resolution usually lead to a better performance particularly for the coarser resolution models. The CMIP6 improvements relative to CMIP5 are systematic across most land regions and are only partially explained by the increase in horizontal resolution, and other differences must therefore help explain the higher CMIP6 skill. Key Points: CMIP5 and CMIP6 models exaggerate the magnitude of daily temperature anomalies for hot days and cold nights extremesHigher‐resolution models improve the simulation of temperature extremes largely due to better simulation of synoptic scalesCMIP6 outperforms the simulation of temperature extremes compared to CMIP5 beyond the benefits given by the higher resolution [ABSTRACT FROM AUTHOR]

Quantifying how climate change drives drought is a priority to inform policy and adaptation planning. We show that the latest Coupled Model Intercomparison Project (CMIP6) simulations project coherent regional patterns in meteorological drought for two emissions scenarios to 2100. We find robust projected changes in seasonal drought duration and frequency (robust over >45% of the global land area), despite a lack of agreement across models in projected changes in mean precipitation (24% of the land area). Future drought changes are larger and more consistent in CMIP6 compared to CMIP5. We find regionalized increases and decreases in drought duration and frequency that are driven by changes in both precipitation mean and variability. Conversely, drought intensity increases over most regions but is not simulated well historically by the climate models. The more robust projections of meteorological drought compared to mean precipitation in CMIP6 provides significant new opportunities for water resource planning. Plain Language Summary: Understanding how climate change affects droughts guides adaptation planning in agriculture, water security, and ecosystem management. Earlier climate projections have highlighted high uncertainty in future drought projections, hindering effective planning. We use the latest projections and find more robust projections of meteorological drought compared to mean precipitation. These more robust projections provide clearer direction for water resource planning and the identification of agricultural and natural ecosystems at risk. Key Points: Quantifying meteorological droughts using changes in both the mean and variability of precipitation leads to more robust projectionsCMIP6 projections show robust changes in the frequency and duration of seasonal meteorological drought over >45% of the global land areaFuture drought changes are larger and more consistent in CMIP6 compared to CMIP5 [ABSTRACT FROM AUTHOR]

Satellite solar‐induced chlorophyll fluorescence (SIF) products from the Global Ozone Monitoring Experiment 2 (GOME‐2) and Orbiting Carbon Observatory 2 (OCO‐2) are used to investigate the responses of vegetation growth to the 2019 heat wave in Australia. Both satellite SIF data sets are more sensitive to water and heat stress than is the greenness‐based vegetation index (enhanced vegetation index). Moreover, the OCO‐2 SIF observations show a more significant reduction and earlier response to the heat stress than does GOME‐2 SIF, indicating that the two satellite SIF data sets differ in how they monitor the drought and heat wave event due to the different timing of observations. Eddy covariance measurements confirm the different responses of dryland vegetation to the 2019 heat wave at a subdaily time scale. The differences in the timing of the satellite SIF products can be used to assess different elements of the impact of heat and water stress on Australian dryland ecosystems. Plain Language Summary: An extreme heat wave occurred across Australia during the summer of 2019, and the high air temperatures and low precipitation affected the vegetation. We used two satellite solar‐induced chlorophyll fluorescence (SIF) data sets to investigate the response of vegetation growth to the extremely high temperatures in Australia. Vegetation growth was reduced during the 2019 summer, but SIF measurements allowed us to separate the impact of the heat wave on the vegetation in the morning and in the afternoon. Our work illustrates how the timing of satellite observations provides different information on how dryland vegetation responds to heat waves in Australia. Key Points: The impact of the 2019 heat wave on Australian dryland ecosystems is examinedSolar‐induced fluorescence (SIF) shows stronger reductions to heat and water stress than does vegetation indexSIF observed at noon responds to the heat stress earlier than does that measured in the morning in Australia [ABSTRACT FROM AUTHOR]

To resolve a series of ecological and environmental problems over the Loess Plateau, the "Grain for Green Program" (GFGP) was initiated at the end of 1990s. Following the conversion of croplands and bare land on hillslopes to forests, the Loess Plateau has displayed a significant greening trend, which has resulted in soil erosion being reduced. However, the GFGP has also affected the hydrology of the Loess Plateau, which has raised questions regarding whether the GFGP should be continued in the future. We investigated the impact of revegetation on the hydrology of the Loess Plateau using relatively high-resolution simulations and multiple realizations with the Weather Research and Forecasting (WRF) model. Results suggest that revegetation since the launch of the GFGP has reduced runoff and soil moisture due to enhanced evapotranspiration. Further revegetation associated with the GFGP policy is likely to further increase evapotranspiration, and thereby reduce runoff and soil moisture. The increase in evapotranspiration is associated with biophysical changes, including deeper roots that deplete deep soil moisture stores. However, despite the increase in evapotranspiration, our results show no impact on rainfall. Our study cautions against further revegetation over the Loess Plateau given the reduction in water available for agriculture and human settlements and the lack of any significant compensation from rainfall. [ABSTRACT FROM AUTHOR]

Key Points: Spatial variability in the land‐atmosphere coupling defines local heatwave sensitivity to antecedent land surface conditionsLand‐driven coupling regions experience a higher heatwave day frequency with temperatures sensitive to prior soil moisture conditionsAntecedent soil moisture anomaly rather than drying rate 2 weeks prior to a heatwave has a longer impact on heatwave temperatures Antecedent land surface conditions play a role in the amplification of temperature anomalies experienced during heatwaves by modifying the local partitioning of available energy between sensible and latent heating. Most existing analyses of heatwave amplification from soil moisture anomalies have focused on exceptionally rare events and consider seasonal scale timescales. However, it is not known how much the daily evolution of land surface conditions, both before and during a heatwave, contributes to the intensity and frequency of these extremes. We examine how the daily evolution of land surface conditions preceding a heatwave event contributes to heatwave intensity. We also diagnose why the land surface contribution to Australian heatwaves is not homogeneous due to spatiotemporal variations in land‐atmosphere coupling. We identify two coupling regimes: a land‐driven regime where surface temperatures are sensitive to local variations in sensible heating and an atmosphere‐driven regime where this is not the case. Northern Australia is consistently strongly coupled, where antecedent soil moisture conditions can influence temperature anomalies up to day 4 of a heatwave. For southern Australia, heatwave temperature anomalies are not influenced by antecedent soil moisture conditions due to an atmosphere‐driven coupling regime. Therefore, antecedent land surface conditions have a role in increasing the temperature anomalies experienced during a heatwave only over regions with strong land‐driven coupling. The timescales over which antecedent land surface conditions contribute to Australian heatwaves also vary regionally. Overall, the spatiotemporal variations of land‐atmosphere interactions help determine where and when antecedent land surface conditions contribute to Australian heat extremes. Plain Language Summary: Research focused on the Northern Hemisphere has demonstrated that unusually dry soils preceding a heatwave event amplify the hot conditions experienced. However, we do not know whether the daily evolution of how the land surface dries out can amplify heatwave temperatures or whether any impact is similar across a large area like Australia. In exploring these knowledge gaps, we find that regions where there is a larger drying trend tend to be more sensitive to land water availability and have more heatwave days. We find that the effect of dry soils before a heatwave varies considerably across Australia. Identifying where dry soils have a large impact on heatwaves required classifying the land into regions where soil water variability affects surface temperatures and where it does not. This could be extended to other atmospheric processes to differentiate between local and remote influences. [ABSTRACT FROM AUTHOR]

To resolve a series of ecological and environmental problems over the Loess Plateau, the Grain for Green Program (GFGP) was initiated at the end of 1990s. Following the conversion of croplands and bare land on hillslopes to forests, the Loess Plateau has displayed a significant greening trend with soil erosion being reduced. However, the GFGP has also affected the hydrology of the Loess Plateau which has raised questions whether the GFGP should be continued in the future. We investigated the impact of revegetation on the hydrology of the Loess Plateau using high resolution simulations and multiple realisations with the Weather Research and Forecasting (WRF) model. Results suggests that land cover change since the launch of the GFGP has reduced runoff and soil moisture due to enhanced evapotranspiration. Further revegetation associated with the GFGP policy is likely to increase evapotranspiration further, and thereby reduce runoff and soil moisture. The increase in evapotranspiration is associated with biophysical changes, including deeper roots that deplete deep soil moisture stores. However, despite the increase in evapotranspiration our results show no impact on rainfall. Our study cautions against further revegetation over the Loess Plateau given the reduction in water available for agriculture and human settlements, without any significant compensation from rainfall. [ABSTRACT FROM AUTHOR]

Land cover type reconstructions, required in climate models, commonly utilize remote sensing products. There are inevitable misclassifications in land cover reconstructions due to the retrieval process. We use the Weather Research and Forecasting model to determine whether these misclassifications can affect the simulations of air temperature and rainfall over the Coordinated Regional Climate Downscaling Experiment (CORDEX) East Asia region, where the accuracy of the land cover classification is low. The Moderate Resolution Imaging Spectroradiometer land cover map is used for the control simulations and is then replaced by the most likely alternative land cover type at pixels where the classification confidence is below various threshold values. Results show that misclassification‐induced land cover change can affect key biogeophysical characteristics (albedo, leaf area index, and roughness length) and these can affect the sensible and latent heat fluxes at regional scales. However, the impact on air temperature is very limited and is restricted to the Tibetan Plateau where warming of up to 2 °C occurs associated with the replacement of barren or sparsely vegetated land to grassland. The impact on rainfall is negligible, and most changes are likely caused by model internal variability rather than land cover change. Overall, uncertainties in the reconstruction of land cover have negligible impacts, and the Moderate Resolution Imaging Spectroradiometer land cover product can be used in regional simulations over East Asia. However, we note that land cover change experiments incorporating uncertainties must utilize large numbers of simulations if air temperature and rainfall changes are to be examined robustly. Key Points: Uncertainties in remotely sensed land cover have a discernible impact on the albedo, leaf area index, and roughness lengthUncertainties in land cover affect the simulation of sensible and latent heat fluxes in some regions depending on how the biogeophysical characteristics changeUncertainties in land cover have negligible impacts on the simulation of air temperature and rainfall in most regions of East Asia [ABSTRACT FROM AUTHOR]

In response to a warming climate, temperature extremes are changing in many regions of the world. Therefore, understanding how the fluxes of sensible heat, latent heat and net ecosystem exchange respond and contribute to these changes is important. We examined 216 sites from the open access Tier 1 FLUXNET2015 and free fair-use La Thuile data sets, focussing only on observed (non-gap-filled) data periods. We examined the availability of sensible heat, latent heat and net ecosystem exchange observations coincident in time with measured temperature for all temperatures, and separately for the upper and lower tail of the temperature distribution, and expressed this availability as a measurement ratio. We showed that the measurement ratios for both sensible and latent heat fluxes are generally lower (0.79 and 0.73 respectively) than for temperature measurements, and the measurement ratio of net ecosystem exchange measurements are appreciably lower (0.42). However, sites do exist with a high proportion of measured sensible and latent heat fluxes, mostly over the United States, Europe and Australia. Few sites have a high proportion of measured fluxes at the lower tail of the temperature distribution over very cold regions (e.g. Alaska, Russia) or at the upper tail in many warm regions (e.g. Central America and the majority of the Mediterranean region), and many of the world's coldest and hottest regions are not represented in the freely available FLUXNET data at all (e.g. India, the Gulf States, Greenland and Antarctica). However, some sites do provide measured fluxes at extreme temperatures, suggesting an opportunity for the FLUXNET community to share strategies to increase measurement availability at the tails of the temperature distribution. We also highlight a wide discrepancy between the measurement ratios across FLUXNET sites that is not related to the actual temperature or rainfall regimes at the site, which we cannot explain. Our analysis provides guidance to help select eddy covariance sites for researchers interested in understanding and/or modelling responses to temperature extremes. [ABSTRACT FROM AUTHOR]

Recent experimental evidence suggests that during heat extremes, wooded ecosystems may decouple photosynthesis and transpiration, reducing photosynthesis to near zero but increasing transpiration into the boundary layer. This feedback may act to dampen, rather than amplify, heat extremes in wooded ecosystems. We examined eddy covariance databases (OzFlux and FLUXNET2015) to identify whether there was field-based evidence to support these experimental findings. We focused on two types of heat extremes: (i) the 3 days leading up to a temperature extreme, defined as including a daily maximum temperature >37 ∘ C (similar to the widely used TXx metric), and (ii) heatwaves, defined as 3 or more consecutive days above 35 ∘ C. When focusing on (i), we found some evidence of reduced photosynthesis and sustained or increased latent heat fluxes at seven Australian evergreen wooded flux sites. However, when considering the role of vapour pressure deficit and focusing on (ii), we were unable to conclusively disentangle the decoupling between photosynthesis and latent heat flux from the effect of increasing the vapour pressure deficit. Outside of Australia, the Tier-1 FLUXNET2015 database provided limited scope to tackle this issue as it does not sample sufficient high temperature events with which to probe the physiological response of trees to extreme heat. Thus, further work is required to determine whether this photosynthetic decoupling occurs widely, ideally by matching experimental species with those found at eddy covariance tower sites. Such measurements would allow this decoupling mechanism to be probed experimentally and at the ecosystem scale. Transpiration during heatwaves remains a key issue to resolve, as no land surface model includes a decoupling mechanism, and any potential dampening of the land–atmosphere amplification is thus not included in climate model projections. [ABSTRACT FROM AUTHOR]

In response to a warming climate, temperature extremes are changing in many regions of the world. Therefore, understanding how the fluxes of sensible heat, latent heat and net ecosystem exchange respond and contribute to these changes is important. We examined 216 sites from the open access Tier 1 FLUXNET2015 and Free-Fair-Use La Thuile datasets, focussing only on observed (non-gap filled) data periods. We examined the availability of sensible heat, latent heat and net ecosystem exchange observations coincident in time with measured temperature for all temperatures, and separately for the upper and lower tail of the temperature distribution and expressed this availability as a measurement ratio. We showed that the measurement ratios for both sensible and latent heat fluxes are generally lower (0.79 and 0.73 respectively) than for temperature, and the measurement ratio of net ecosystem exchange measurements are appreciably lower (0.42). However, sites do exist with a high proportion of measured sensible and latent heat fluxes, mostly over the United States, Europe and Australia. Few sites have a high proportion of measured fluxes at the lower tail of the temperature distribution over very cold regions (e.g. Alaska, Russia) and at the upper tail in many warm regions (e.g. Central America and the majority of the Mediterranean region), and many of the world's coldest and hottest regions are not represented in the freely available FLUXNET data at all (e.g. India, the Gulf States, Greenland and Antarctica). However, some sites do provide measured fluxes at extreme temperatures suggesting an opportunity for the FLUXNET community to share strategies to increase measurement availability at the tails of the temperature distribution. We also highlight a wide discrepancy between the measurement ratios across FLUXNET sites that is not related to the actual temperature or rainfall regimes at the site, which we cannot explain. Our analysis provides guidance to help select eddy covariance sites for researchers interested in exploring responses to temperature extremes. [ABSTRACT FROM AUTHOR]

Recent experimental evidence suggests that during heat extremes, wooded ecosystems may decouple photosynthesis and transpiration: reducing photosynthesis to near zero but increasing transpiration into the boundary layer. This feedback may act to dampen, rather than amplify, heat extremes in wooded ecosystems. We examined eddy-covariance databases (OzFlux and FLUXNET2015) to identify whether there was field-based evidence to support these experimental findings. We focused on two types of heat extremes: (i) the three days leading up to a temperature extreme, defined as including a daily maximum temperature > 37 °C (similar to the widely used TXx metric) and (ii) heatwaves, defined as three or more consecutive days above 35 °C. When focussing on (i), we found some evidence of reduced photosynthesis and sustained or increased latent heat fluxes in seven Australian evergreen wooded flux sites. However, when considering the role of vapour pressure deficit and focusing on (ii), we were unable to conclusively disentangle the decoupling between photosynthesis and latent heat flux from the effect of increasing vapour pressure deficit. Outside of Australia, the Tier-1 FLUXNET2015 database provided limited scope to tackle this issue as it does not sample sufficient high temperature events with which to probe the physiological response of trees to extreme heat. Thus, further work is required to determine whether this photosynthetic decoupling occurs widely, ideally by matching experimental species with those found at eddy-covariance towers sites. Such measurements would allow this decoupling mechanism to be probed experimentally and at the ecosystem scale. Transpiration during heatwaves remains a key issue to resolve, as no land surface model includes a decoupling mechanism, and any potential dampening of the land-atmosphere amplification is thus not included in climate model projections. [ABSTRACT FROM AUTHOR]

We examine the joint contribution of urban expansion and climate change on heat stress over the Sydney region. A Regional Climate Model was used to downscale present (1990–2009) and future (2040–2059) simulations from a Global Climate Model. The effects of urban surfaces on local temperature and vapor pressure were included. The role of urban expansion in modulating the climate change signal at local scales was investigated using a human heat-stress index combining temperature and vapor pressure. Urban expansion and climate change leads to increased risk of heat-stress conditions in the Sydney region, with substantially more frequent adverse conditions in urban areas. Impacts are particularly obvious in extreme values; daytime heat-stress impacts are more noticeable in the higher percentiles than in the mean values and the impact at night is more obvious in the lower percentiles than in the mean. Urban expansion enhances heat-stress increases due to climate change at night, but partly compensates its effects during the day. These differences are due to a stronger contribution from vapor pressure deficit during the day and from temperature increases during the night induced by urban surfaces. Our results highlight the inappropriateness of assessing human comfort determined using temperature changes alone and point to the likelihood that impacts of climate change assessed using models that lack urban surfaces probably underestimate future changes in terms of human comfort. [ABSTRACT FROM AUTHOR]

The fire weather of south-east Australia from 1985 to 2009 has been simulated using the Weather Research and Forecasting (WRF) model. The US National Oceanic and Atmospheric Administration Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis supplied the lateral boundary conditions and initial conditions. The model simulated climate and the reanalysis were evaluated against station-based observations of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual cumulative FFDI and days per year with FFDI above 50. WRF simulated the main features of the FFDI distribution and its spatial variation, with an overall positive bias. Errors in average FFDI were caused mostly by errors in the ability of WRF to simulate relative humidity. In contrast, errors in extreme FFDI values were driven mainly by WRF errors in wind speed simulation. However, in both cases the quality of the observed data is difficult to ascertain. WRF run with 50-km grid spacing did not consistently improve upon the reanalysis statistics. Decreasing the grid spacing to 10 km led to fire weather that was generally closer to observations than the reanalysis across the full range of evaluation metrics used here. This suggests it is a very useful tool for modelling fire weather over the entire landscape of south-east Australia. [ABSTRACT FROM AUTHOR]