Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product

No synthesized global gridded runoff product, derived from multiple sources, is available, despite such a product being useful for meeting the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-sample tests to examine the success of the dissimilarity approach, and we confirm that the weighted product performs better than its 11 constituent products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly timescales, and includes time-variant uncertainty, for the period 1980–2012 on a 0.5∘ grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents the seasonal runoff cycle for most of the globe well. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f9617_9854_8096_5291 (last access: 31 January 2019).</p

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.</p

The College News, 1923-01-24, Vol. 09, No. 13

Bryn Mawr College student newspaper. Merged with The Haverford News in 1968 to form the Bi-college News (with various titles from 1968 on). Published weekly (except holidays) during the academic year

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those ‘next users’ of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem\u27s carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers

Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, in addition to being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information on the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in four common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product

Linear Optimal Runoff Aggregate (LORA): A global gridded synthesis runoff product

No synthesized global gridded runoff product, derived from multiple sources, is available, despite such a product being useful for meeting the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-sample tests to examine the success of the dissimilarity approach, and we confirm that the weighted product performs better than its 11 constituent products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly timescales, and includes time-variant uncertainty, for the period 1980-2012 on a 0.5ĝ grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents the seasonal runoff cycle for most of the globe well. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f9617-9854-8096-5291 (last access: 31 January 2019)

Time Variability Correction of CMIP6 Climate Change Projections

Abstract Accurate projections of climate change and associated extreme events under differing emission scenarios are linked to realistic representations of the temporal variability of the atmosphere at a variety of time scales, for example, annual, seasonal, synoptic, and daily. Here a new method is employed to explicitly quantify a model's ability to accurately represent covariance at and between differing time scales. From our global‐scale analysis, on average, raw Coupled Model Intercomparison Project Phase 6 (CMIP6) models misrepresent temporal variances at differing time scales for maximum temperature (tasmax) by a considerable margin, particularly at 183‐, 92‐ and 46‐day time scales. To ameliorate such variability errors, we propose a novel Time Variability Correction (TVC) method that corrects temporal covariances while preserving the essential time‐event sequence of the model simulations. We adopt a model‐as‐truth framework to evaluate the effectiveness of the TVC method under future forcing conditions when applied to daily tasmax simulations from 23 CMIP6 models for 1% of the global grid cells. TVC‐corrected temperatures generally show improved matching of temporal variance and lag correlations in the out‐of‐sample projection period compared to simple mean‐corrected projections. By imparting more realistic temporal‐correlations to model series, TVC is expected to improve the projections of extreme events associated with persistent heat, such as heatwaves. Applying TVC to future temperature projections using actual observations significantly increases the temperature variance in most middle to high latitude land regions in the Northern Hemisphere, while decreasing it in most low to middle latitude land regions, compared to simple mean‐corrected projections

Using Machine Learning to Cut the Cost of Dynamical Downscaling

Global climate models (GCMs) are commonly downscaled to understand future local climate change. The high computational cost of regional climate models (RCMs) limits how many GCMs can be dynamically downscaled, restricting uncertainty assessment. While statistical downscaling is cheaper, its validity in a changing climate is unclear. We combine these approaches to build an emulator leveraging the merits of dynamical and statistical downscaling. A machine learning model is developed for each coarse grid cell to predict fine grid variables, using coarse-scale climate predictors with fine grid land characteristics. Two RCM emulators, one Multilayer Perceptron (MLP) and one Multiple Linear Regression error-reduced with Random Forest (MLR-RF), are developed to downscale daily evapotranspiration from 12.5 km (coarse-scale) to 1.5 km (fine-scale). Out-of-sample tests for the MLP and MLR-RF achieve Kling-Gupta-Efficiency of 0.86 and 0.83, correlation of 0.89 and 0.86, and coefficient of determination (R2) of 0.78 and 0.75, with a relative bias of −6% to 5% and −5% to 4%, respectively. Using histogram match for spatial efficiency, both emulators achieve a median score of ∼0.77. This is generally better than a common statistical downscaling method in a range of metrics. Additionally, through “spatial transitivity,” we can downscale GCMs for new regions at negligible cost and only minor performance loss. The framework offers a cheap and quick way to downscale large ensembles of GCMs. This could enable high-resolution climate projections from a larger number of global models, enabling uncertainty quantification, and so better support for resilience and adaptation planning

Exploring how groundwater buffers the influence of heatwaves on vegetation function during multi-year droughts

The co-occurrence of droughts and heatwaves can have significant impacts on many socioeconomic and environmental systems. Groundwater has the potential to moderate the impact of droughts and heatwaves by moistening the soil and enabling vegetation to maintain higher evaporation, thereby cooling the canopy. We use the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model, coupled to a groundwater scheme, to examine how groundwater influences ecosystems under conditions of co-occurring droughts and heatwaves. We focus specifically on south-east Australia for the period 2000–2019, when two significant droughts and multiple extreme heatwave events occurred. We found groundwater plays an important role in helping vegetation maintain transpiration, particularly in the first 1–2 years of a multi-year drought. Groundwater impedes gravity-driven drainage and moistens the root zone via capillary rise. These mechanisms reduced forest canopy temperatures by up to 5 ∘C during individual heatwaves, particularly where the water table depth is shallow. The role of groundwater diminishes as the drought lengthens beyond 2 years and soil water reserves are depleted. Further, the lack of deep roots or stomatal closure caused by high vapour pressure deficit or high temperatures can reduce the additional transpiration induced by groundwater. The capacity of groundwater to moderate both water and heat stress on ecosystems during simultaneous droughts and heatwaves is not represented in most global climate models, suggesting that model projections may overestimate the risk of these events in the future.</p

New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales