Earth System Simulator Research Articles

ACCESS datasets for CMIP6: methodology and idealised experiments

The Australian Community Climate and Earth System Simulator (ACCESS) has contributed to the World Climate Research Programme’s Coupled Model Intercomparison Project Phase 6 (CMIP6) using two fully coupled model versions (ACCESS-CM2 and ACCESS-ESM1.5) and two ocean–sea-ice model versions (1° and 0.25° resolution versions of ACCESS-OM2). The fully coupled models differ primarily in the configuration and version of their atmosphere components (including the aerosol scheme), with smaller differences in their sea-ice and land model versions. Additionally, ACCESS-ESM1.5 includes biogeochemistry in the land and ocean components and can be run with an interactive carbon cycle. CMIP6 comprises core experiments and associated thematic Model Intercomparison Projects (MIPs). This paper provides an overview of the CMIP6 submission, including the methods used for the preparation of input forcing datasets and the post-processing of model output, along with a comprehensive list of experiments performed, detailing their initialisation, duration, ensemble number and computational cost. A small selection of model output is presented, focusing on idealised experiments and their variants at global scale. Differences in the climate simulation of the two coupled models are highlighted. ACCESS-CM2 produces a larger equilibrium climate sensitivity (4.7°C) than ACCESS-ESM1.5 (3.9°C), likely a result of updated atmospheric parameterisation in recent versions of the atmospheric component of ACCESS-CM2. The idealised experiments run with ACCESS-ESM1.5 show that land and ocean carbon fluxes respond to both changing atmospheric CO2 and to changing temperature. ACCESS data submitted to CMIP6 are available from the Earth System Grid Federation (https://doi.org/10.22033/ESGF/CMIP6.2281 and https://doi.org/10.22033/ESGF/CMIP6.2288). The information provided in this paper should facilitate easier use of these significant datasets by the broader climate community.

Evaluation of climate variability and change in ACCESS historical simulations for CMIP6

We analyse and document the historical simulations performed by two versions of the Australian Community Climate and Earth System Simulator (ACCESS-CM2 and ACCESS-ESM1.5) for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Three ensemble members from each model are used to compare the simulated seasonal-mean climate, climate variability and climate change with observations over the historical period. Where appropriate, we also compare the ACCESS model results with the results from 36 other CMIP6 models. We find that the simulations of the winter and summer mean climates (over the global domain) by the two ACCESS models are similar to or better than most of the other CMIP6 models for surface temperature, precipitation and surface specific humidity. For sea-level pressure, both ACCESS models perform worse than most other models. The spatial structures of the prominent climate variability modes (ENSO, IOD, IPO and AMO) also compare favourably with the corresponding observed structures. However, the results for the simulation of the models’ temporal variability are mixed. In particular, whereas ACCESS-ESM1.5 simulates ENSO events with ~3-year periods (that are closer to the observed periods of 3–7 years), the ACCESS-CM2 simulates ENSO events having quasi-biennial periods. However, ACCESS-CM2 has a much smaller bias (−0.1 W m−2) in present-day top-of-the-atmosphere energy balance than ACCESS-ESM1.5 (−0.6 W m−2). The ACCESS models simulate the anthropogenic climate change signal in historical global-mean surface temperature reasonably well, although the simulated signal variances are ~10% weaker than the observed signal variance (a common bias in most CMIP6 models). Both models also well simulate the major features of observed surface temperature changes, as isolated using a multiple regression model. Despite some identified biases, the two ACCESS models provide high-quality climate simulations that may be used in further analyses of climate variability and change.

Simulations of the Waroona fire using the coupled atmosphere–fire model ACCESS-Fire

The Waroona fire burned 69 000 ha south of Perth in January 2016. There were two fatalities and 170 homes were lost. Two evening ember storms were reported and pyrocumulonimbus (pyroCb) cloud developed on consecutive days. The extreme fire behaviour did not reconcile with the near- surface conditions customarily used to assess fire danger. A case study of the fire (Peace et al. 2017) presented the hypothesis that the evening ember storms resulted from interactions between the above-surface wind fields, local topography and the fire plume. The coupled fire–atmosphere model ACCESS-Fire has been run in order to explore this hypothesis and other aspects of the fire activity, including the pyroCb development. ACCESS-Fire incorporates the numerical weather prediction model ACCESS (Australian Community Climate and Earth System Simulator, described by Puri et al. 2013) and a fire spread component. In these simulations, the Dry Eucalypt Forest Fire (Vesta) fire spread model is used. In this study we first show that the reconstruction of surface fire spread and simulated fire spread are a good match for the first day; second, we show that the model produces deep moist convection as an indicator of pyrocumulonimbus cloud and, third, we show the fire–atmosphere interactions surrounding the ember showers provided an environment conducive to the observed mass spotting. The simulation results demonstrate that ACCESS-Fire is a tool that may be used to further explore the complex processes and potential impacts surrounding pyroCb development and short-distance ember transport.

Forecasting Northern Australian Summer Rainfall Bursts Using a Seasonal Prediction System

Abstract Rainfall bursts are relatively short-lived events that typically occur over consecutive days, up to a week. Northern Australian industries like sugar farming and beef are highly sensitive to burst activity, yet little is known about the multiweek prediction of bursts. This study evaluates summer (December–March) bursts over northern Australia in observations and multiweek hindcasts from the Bureau of Meteorology’s multiweek to seasonal system, the Australian Community Climate and Earth-System Simulator, Seasonal version 1 (ACCESS-S1). The main objective is to test ACCESS-S1’s skill to confidently predict tropical burst activity, defined as rainfall accumulation exceeding a threshold amount over three days, for the purpose of producing a practical, user-friendly burst forecast product. The ensemble hindcasts, made up of 11 members for the period 1990–2012, display good predictive skill out to lead week 2 in the far northern regions, despite overestimating the total number of summer burst days and the proportion of total summer rainfall from bursts. Coinciding with a predicted strong Madden–Julian oscillation (MJO), the skill in burst event prediction can be extended out to four weeks over the far northern coast in December; however, this improvement is not apparent in other months or over the far northeast, which shows generally better forecast skill with a predicted weak MJO. The ability of ACCESS-S1 to skillfully forecast bursts out to 2–3 weeks suggests the bureau’s recent prototype development of a burst potential forecast product would be of great interest to northern Australia’s livestock and crop producers, who rely on accurate multiweek rainfall forecasts for managing business decisions.

APS2-ACCESS-C2: the first Australian operational NWP convection-permitting model

The Australian Bureau of Meteorology’s ‘Australian Parallel Suite’ (APS) operational numerical weather prediction regional Australian Community Climate and Earth-System Simulator (ACCESS) city-based system (APS1 ACCESS-C1) was updated in August 2017 with the commissioning of the APS2 ACCESS-C2. ACCESS-C2 runs over six regional domains. Significant upgrade changes included implementation of Unified Model 8.2 code; nesting in the 12 km resolution APS2 ACCESS-R2 regional model; and, importantly, an increased horizontal resolution from 4 to 1.5 km, enabling C2 to become the first Australian operational convection-permitting model (CPM). Traditional rainfall verification metrics and Fractions Skill Score show C2 forecast skill over ACCESS-C domains in summer and winter was generally, and in many cases, significantly better than C1. Case studies showed that C2 forecasts had better-detailed wind and precipitation fields, particularly at longer forecast ranges and higher rain rates. The improvements in C2 forecasts were principally due to its CPM ability to simulate high temporal and spatial resolution features, which continue to be of great interest to forecasters. C2 also laid the groundwork for the present day APS3 ACCESS-C forecast C3 and ensemble CE3 models and further development of higher resolution (down to 300 m) fire weather and urban models.

Response of Tropical Cyclone Frequency to Sea Surface Temperatures Using Aqua-Planet Simulations

The present study investigates the effect of increasing sea surface temperatures (SSTs) on tropical cyclone (TC) frequency using the high-resolution Australian Community Climate and Earth-System Simulator (ACCESS) model. We examine environmental conditions leading to changes in TC frequency in aqua-planet global climate model simulations with globally uniform sea surface temperatures (SSTs). Two different TC tracking schemes are used. The Commonwealth Scientific and Industrial Research Organization (CSIRO) scheme (a resolution-dependent scheme) detects TCs that resemble observed storms, while the Okubo–Weiss zeta parameter (OWZP) tracking scheme (a resolution-independent scheme) detects the locations within “marsupial pouches” that are favorable for TC formation. Both schemes indicate a decrease in the global mean TC frequency with increased saturation deficit and static stability of the atmosphere. The OWZP scheme shows a poleward shift in the genesis locations with rising temperatures, due to lower vertical wind shear. We also observe an overall decrease in the formation of tropical depressions (TDs) with increased temperatures, both for those that develop into TCs and non-developing cases. The environmental variations at the time of TD genesis between the developing and the non-developing tropical depressions identify the Okubo–Weiss (OW) parameter and omega (vertical mass flux) as significant influencing variables. Initial vortices with lower vorticity or with weaker upward mass flux do not develop into TCs due to environments with higher saturation deficit and stronger static stability of the atmosphere. The latitudinal variations in the large-scale environmental conditions account for the latitudinal differences in the TC frequency in the OWZP scheme.

Subseasonal prediction of the 2020 Great Barrier Reef and Coral Sea marine heatwave

The 2020 marine heatwave (MHW) in the Great Barrier Reef (GBR) and Coral Sea led to mass coral bleaching. Sea surface temperature anomalies reached +1.7 °C for the whole of the GBR and Coral Sea and exceeded +2 °C across broad regions (referenced to 1990–2012). The MHW reached Category 2 (Strong) and warm anomalies peaked between mid-February and mid-March 2020. The MHW’s peak intensity aligned with regions of reduced cloud cover and weak wind speeds. We used a MHW framework to assess the ability of an operational coupled ocean-atmosphere prediction system (Australian Community Climate and Earth System Simulator Seasonal version 1) to capture the MHW’s severity, duration, and spatial extent. For initial week predictions, the predicted MHW severity generally agreed with the magnitude and spatial extent of the observed severity for that week. The model ensemble mean did not capture the MHW’s development phase at lead times beyond the first week. The model underestimated the MHW’s spatial extent, which reached up to 95% of the study area with at least Moderate severity and up to 43% with at least Strong severity. However, most forecast ensemble members correctly predicted the period of Strong severity in the first week of the model forecast. The model correctly predicted MHW conditions to persist from mid-February to mid-March but did not capture the end of the MHW. The inability to predict the end of the event and other periods of less skilful prediction were related to subseasonal variability owing to weather systems, including the passage of tropical cyclones not simulated in the model. On subseasonal time scale, evaluating daily to weekly forecasts of ocean temperature extremes is an important step toward implementing methods for developing operational forecast extremes products for use in early warning systems.

Evaluation of Six Satellite-Based Terrestrial Latent Heat Flux Products in the Vegetation Dominated Haihe River Basin of North China

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p < 0.01) and lowest root mean square error (RMSE) (28.2 W/m2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget.

Spatiotemporal variations of water productivity for cropland and driving factors over China during 2001–2015

Croplands play an important role in China’s agricultural production and food security. However, the shortage of water resource due to climate change and unsuitable utilization poses heavy pressure on agricultural water use in China. Water productivity (WP), defined as the amount of crop production per unit of water consumption by croplands, provides a useful indicator for quantifying where the water can be used more effectively. To date, the spatiotemporal variations of cropland WP in China and its controlling factors at the interannual scale remain poorly understood. In this study, a remote sensing-based ecosystem model (i.e., Breathing Earth System Simulator, BESS) was applied to quantify and analyze the spatiotemporal variations and driving factors of cropland WP in China during 2001–2015. The results showed cropland WP in China had high spatial heterogeneity, ranging from 0.27 to 3.91gC kg−1 H2O with an average of 1.86 ± 0.30 gC kg−1 H2O. Dry farmland and paddy field differed considerably in WP values across different regions. During 2001–2015, WP of most croplands (88%) exhibited significantly increasing trends, and dry farmland generally had greater increasing trends than paddy field among all regions. Contribution analysis revealed that the spatiotemporal variations of cropland WP during 2001–2015 were mostly attributed to remarkable increase of crop yield (i.e., GPP), except for some croplands in northwestern regions (e.g., GX) where WP variations were regulated by cropland water consumption (i.e., ET). Furthermore, we examined the driving factors of cropland WP interannual varibility (IAV), and found the dominant factor of WP IAV varied greatly between cropland types and regions. Overall, precipitation was the primary driver of cropland WP IAV at the national level, followed by air temperature and solar radiation. Besides, drought also plays a great role in manipulating WP IAV, especially the medium and long-term drought.

A 1 km global cropland dataset from 10 000 BCE to 2100 CE

Abstract. Cropland greatly impacts food security, energy supply, biodiversity, biogeochemical cycling, and climate change. Accurately and systematically understanding the effects of agricultural activities requires cropland spatial information with high resolution and a long time span. In this study, the first 1 km resolution global cropland proportion dataset for 10 000 BCE–2100 CE was produced. With the cropland map initialized in 2010 CE, we first harmonized the cropland demands extracted from the History Database of the Global Environment 3.2 (HYDE 3.2) and the Land-Use Harmonization 2 (LUH2) datasets and then spatially allocated the demands based on the combination of cropland suitability, kernel density, and other constraints. According to our maps, cropland originated from several independent centers and gradually spread to other regions, influenced by some important historical events. The spatial patterns of future cropland change differ in various scenarios due to the different socioeconomic pathways and mitigation levels. The global cropland area generally shows an increasing trend over the past years, from 0×106 km2 in 10 000 BCE to 2.8×106 km2 in 1500 CE, 6.2×106 km2 in 1850 CE, and 16.4×106 km2 in 2010 CE. It then follows diverse trajectories under future scenarios, with the growth rate ranging from 16.4 % to 82.4 % between 2010 CE and 2100 CE. There are large area disparities among different geographical regions. The mapping result coincides well with widely used datasets at present in both distribution pattern and total amount. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The spatiotemporally continuous and conceptually consistent global cropland dataset serves as a more comprehensive alternative for long-term earth system simulations and other precise analyses. The flexible and efficient harmonization and downscaling framework can be applied to specific regions or extended to other land use and cover types through the adjustable parameters and open model structure. The 1 km global cropland maps are available at https://doi.org/10.5281/zenodo.5105689 (Cao et al., 2021a).

Improvements in subseasonal forecasts of rainfall extremes by statistical postprocessing methods

End users of seasonal rainfall forecasts demand not only skilful forecasts of rainfall totals but also skilful forecasts of rainfall extremes. For better forecasts of rainfall extremes, the copula-based postprocessing (CPP) method, which was originally designed for forecasting rainfall totals, is modified with a hybrid probability distribution to model low-to-medium and heavy rainfall separately and to allow the forecast of extreme rainfall events that have never occurred in observed records. A case study for 17 rainfall stations in Queensland, Australia is carried out to test the forecast performance of the modified CPP to postprocess the raw Australian Community Climate and Earth-System Simulator (ACCESS) Seasonal model (ACCESS-S) seasonal rainfall forecast at a daily scale. The modified CPP improves the overall skill of forecasting 12 rainfall indices from the raw forecast and outperforms two quantile mapping based methods in most cases. The use of the hybrid distribution leads to more promising forecast skill for heavy and very heavy rainfall related indices. The forecast skill decreases with longer lead times and the modified CPP leads to neutral forecasts (i.e. forecasts with skill similar to climatology forecasts) for most rainfall indices beyond 0-month lead time. The skill improvement has been found in all selected climate regions and initialisation dates (from the 1st day of each month), though more substantial improvement is observed in the rainfall stations within the tropical zone where the raw forecast is particularly unskilful.

Comprehensive assessment of five global daily downward shortwave radiation satellite products

The downward shortwave radiation (DSR) is a critical parameter of the surface radiation budget. Several DSR satellite products have been developed in recent years. In this study, five updated global satellite daily DSR products were evaluated using in situ measurements from 142 global sites with a special focus on high latitudes in 2004. These five products are Clouds and the Earth's Radiant Energy System Synoptic TOA and surface fluxes and clouds (CERES), Clouds, Albedo and Radiation Edition 2 data (CLARA), Global Land Surface Satellite Downward Shortwave Radiation (GLASS), Breathing Earth System Simulator (BESS) shortwave radiation product, and Moderate Resolution Imaging Spectroradiometer land surface Downward Shortwave Radiation (MCD18) with a spatial resolution of 100, 25, 5, 5, and 1 km, respectively. The CERES, BESS, and MCD18 provide full global coverage throughout the year, whereas CLARA and GLASS present different levels of seasonal data loss over high-latitude areas. The products were aggregated and compared at various spatial resolutions over different subareas. The overall accuracy increased after the products were aggregated to 100 km. However, the highest accuracy was achieved at a resolution of 25 km over high-latitude areas for GLASS and MCD18. When all products were evaluated at a resolution of 100 km, the global root-mean-square error of CERES, CLARA, GLASS, BESS, and MCD18 was 27.6, 29.1, 30.3, 29.6, and 31.6 W/m2, respectively, and the mean bias difference was 2.2, −1.5, −1.8, −3.4, and −8.0 W/m2. The accuracies of most products are ~7 W/m2 lower over high-latitude areas. A seasonal variation of the accuracies was observed for all products. It is particularly pronounced over high-latitude areas. With respect to the long term, both in situ data, BESS, and CERES show insignificant trends, while CLARA and GLASS present dimming trend. Besides, CLARA and GLASS exhibit slight annual changes of −0.250 and −0.387 W/m2 in the bias and 0.357 and 0.310 Wm−2 in the RMSE in the past two decades. GLASS and MCD18 exhibit a superior performance over coastal regions but degrade over snow-covered areas. Potential refinements of current high-resolution DSR retrieval algorithms are suggested, which will improve the retrieval accuracy. Highly accurate products with a long-term stability, especially over high-latitude areas, are required for future climate change analyses.

A New Operational Seasonal Thermal Stress Prediction Tool for Coral Reefs Around Australia

Seasonal forecasts of sea surface temperature (SST) have become increasingly important tools in recent years for reef managers to help inform and coordinate management responses to mass coral bleaching events. This manuscript presents new operational thermal stress forecast products for prediction of coral bleaching risk, based on the seasonal ensemble prediction system ACCESS-S1 (Australian Community Climate and Earth System Simulator–Seasonal Version 1). These accumulated thermal stress products form critical tools for reef management, providing advance warning of high thermal stress, and increased risk of coral bleaching in the coming season. Degree Heating Months (DHM) consider both the magnitude and duration of thermal stress, both of which are important in determining reef impacts. Both hindcast and operational realtime DHM forecasts are assessed for past bleaching events across Australia, and the impacts of different drivers and local forcings between regions compared. Generally, the model has the highest skill when forecasting events driven by large scale climate drivers such as the El Niño Southern Oscillation (ENSO) which impacts coral reefs on all sides of Australia. ACCESS-S1 hindcasts indicate higher skill on the west Australian coast than the Great Barrier Reef for summer months, except for the North West Shelf. Realtime forecasts of the 2020 Great Barrier Reef coral bleaching event, used operationally by reef managers throughout this event, are also presented. This work advances our understanding of the 2020 event, provides skill assessments for the new DHM products, and discusses the use of a stationary baseline in a changing climate. High DHM values can indicate an increased risk of marine heatwaves, which are likely to have increasing impacts on Australia’s reef systems in the future under a warming climate.

Super Residual Circulation: a new perspective on ocean vertical heat transport

AbstractOcean circulation and mixing regulate Earth’s climate by moving heat vertically within the ocean. We present a new formalism to diagnose the role of ocean circulation and diabatic processes in setting vertical heat transport in ocean models. In this formalism we use temperature tendencies, rather than explicit vertical velocities to diagnose circulation. Using quasi-steady state simulations from the Australian Community Climate and Earth-System Simulator Ocean Model (ACCESS-OM2), we diagnose a diathermal overturning circulation in temperature-depth space. Furthermore, projection of tendencies due to diabatic processes onto this coordinate permits us to represent these as apparent overturning circulations. Our framework permits us to extend the concept of Super-Residual Transport (SRT), which combines mean and eddy advection terms with subgridscale isopycnal mixing due to mesoscale eddies, but excludes small-scale three dimensional turbulent mixing effect, to construct a new overturning circulation – the ‘Super Residual Circulation’ (SRC).We find that in the coarse resolution version of ACCESS-OM2 (nominally 1° horizontal resolution) the SRC is dominated by an ~11 Sv circulation which transports heat upward. The SRC’s upward heat transport is ~2 times larger in a finer horizontal resolution (0.1°) version of ACCESS, suggesting a differing balance of super-residual and parameterized small-scale processes may emerge as eddies are resolved. Our analysis adds new insight into super-residual processes, as the SRC elucidates the pathways in temperature and depth space along which watermass transformation occurs.

Assessing and improving cloud-height-based parameterisations of global lightning flash rate, and their impact on lightning-produced NO<sub><i>x</i></sub> and tropospheric composition in a chemistry–climate model

Abstract. Although lightning-generated oxides of nitrogen (LNOx) account for only approximately 10 % of the global NOx source, they have a disproportionately large impact on tropospheric photochemistry due to the conducive conditions in the tropical upper troposphere where lightning is mostly discharged. In most global composition models, lightning flash rates used to calculate LNOx are expressed in terms of convective cloud-top height via the Price and Rind (1992) (PR92) parameterisations for land and ocean, where the oceanic parameterisation is known to greatly underestimate flash rates. We conduct a critical assessment of flash-rate parameterisations that are based on cloud-top height and validate them within the Australian Community Climate and Earth System Simulator – United Kingdom Chemistry and Aerosol (ACCESS-UKCA) global chemistry–climate model using the Lightning Imaging Sensor and Optical Transient Detector (LIS/OTD) satellite data. While the PR92 parameterisation for land yields satisfactory predictions, the oceanic parameterisation, as expected, underestimates the observed flash-rate density severely, yielding a global average over the ocean of 0.33 flashes s−1 compared to the observed 9.16 flashes s−1 and leading to LNOx being underestimated proportionally. We formulate new flash-rate parameterisations following Boccippio's (2002) scaling relationships between thunderstorm electrical generator power and storm geometry coupled with available data. The new parameterisation for land performs very similarly to the corresponding PR92 one, as would be expected, whereas the new oceanic parameterisation simulates the flash-rate observations much more accurately, giving a global average over the ocean of 8.84 flashes s−1. The use of the improved flash-rate parameterisations in ACCESS-UKCA changes the modelled tropospheric composition – global LNOx increases from 4.8 to 6.6 Tg N yr−1; the ozone (O3) burden increases by 8.5 %; there is an increase in the mid- to upper-tropospheric NOx by as much as 40 pptv, a 13 % increase in the global hydroxyl radical (OH), a decrease in the methane lifetime by 6.7 %, and a decrease in the lower-tropospheric carbon monoxide (CO) by 3 %–7 %. Compared to observations, the modelled tropospheric NOx and ozone in the Southern Hemisphere and over the ocean are improved by this new flash-rate parameterisation.

Achieving effective calibration of precipitation forecasts over a continental scale

Study regionAustralia. Study focusWe developed the Seasonally Coherent Calibration (SCC) model to post-process precipitation forecasts from numerical weather prediction (NWP) models. The SCC model is capable of generating high-quality calibrated forecasts which are coherent in climatology, including seasonality, consistent with observations, despite only a short period of forecasts being available. In this study, we post-process deterministic precipitation forecasts from the Australian Bureau of Meteorology’s Australian Community Climate and Earth System Simulator G2 version (ACCESS-G2) NWP model to generate calibrated ensemble forecasts across Australia. We evaluate the effectiveness, robustness, and computational feasibility of forecast post-processing with the SCC model at a high spatial resolution across the continental scale. New hydrological insights for the regionInadequate representation of forecast seasonality in calibration models poses a challenge for the calibration of NWP forecasts. Through this investigation, the statistical assumptions and reparameterization algorithms of the SCC model are confirmed to be effective and robust in addressing the challenge. Calibrated precipitation forecasts could be used to support operational water balance forecasting in Australia. Post-processing of NWP precipitation forecasts with the SCC model will also benefit a broad range of forecast users in Australia by providing accurate, skillful, and reliable ensemble forecasts. Computation strategies adopted in this study will help develop effective procedures for future post-processing of NWP forecasts with sophisticated calibration models at a large spatial scale.

Spatiotemporal Patterns of Ecosystem Restoration Activities and Their Effects on Changes in Terrestrial Gross Primary Production in Southwest China

Large-scale ecosystem restoration projects (ERPs) have been implemented since the beginning of the new millennium to restore vegetation and improve the ecosystem in Southwest China. However, quantifying the effects of specific restoration activities, such as afforestation and grass planting, on vegetation recovery is difficult due to their incommensurable spatiotemporal distribution. Long-term and successive ERP-driven land use/cover changes (LUCCs) were used to recognise the spatiotemporal patterns of major restoration activities, and a contribution index was defined to assess the effects of these activities on gross primary production (GPP) dynamics in Southwest China during the period of 2001–2015. The results were as follows. (1) Afforestation and grass planting were major restoration activities that accounted for more than 54% of all LUCCs in Southwest China. Approximately 96% of restoration activities involved afforestation, and these activities were mostly distributed around Yunnan Province. (2) The Breathing Earth System Simulator (BESS) GPP performed better than the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP validated by field observation data. Nevertheless, their annual GPP trends were similar and increased by 12,581 g C m−2 d−1 and 13,406 g C m−2 d−1 for MODIS and BESS GPPs, respectively. (3) Although the afforestation and grass planting areas accounted for less than 1% of the total area of Southwest China, they contributed to more than 1% of the annual GPP increase in the entire study area. Afforestation directly contributed 14.94% (BESS GPP) or 24.64% (MODIS GPP) to the annual GPP increase. Meanwhile, grass planting directly contributed only 0.41% (BESS GPP) or 0.03% (MODIS GPP) to the annual GPP increase.

Monthly rainfall forecasting using temperature and climate indices through a hybrid method in Queensland, Australia

AbstractThis paper presents applications of wavelet artificial neural networks (WANN) to forecast rainfalls one, three, six, and twelve months in advance using lagged monthly rainfall, maximum, minimum temperatures, Southern Oscillation Index (SOI), Inter-decadal Pacific Oscillation (IPO), and Nino3.4 as predictors. Eight input datasets comprised of different combinations of predictive variables were used for ten candidate climate stations in Queensland, Australia. Datasets were split as 1908 to 1999 for the training of the model and 2000 to 2016 for the verification of the model. Also, the conventional Artificial Neural Network (ANN) model was developed with the same input datasets to compare with WANN results. Moreover, the skillfulness of the WANN was investigated with the current climate prediction system used by the Australian Bureau of Meteorology (BOM), Australian Community Climate Earth-System Simulator–Seasonal (ACCESS–S) as well as climatology forecasts. The comparisons showed that the WANN achieved the lowest errors for three-month lagged prediction with an average Root Mean Square Error (RMSE) of 38.6mm. In contrast, for the same lag-period, the average RMSEs from ANN, ACCESS-S, and climatology predictions were 72.2mm, 102.7mm, and 72.2mm, respectively. It is also found that the ANN underestimates the peak values with an average value of 49%, 47%, 52%, and 53% at one, three, six, and twelve months lead times, correspondingly. However, the corresponding peak values underestimation through the WANN were 0%, 1%, 22%, and 39%, respectively. This research provides promising insights into using hybrid methods for predicting rainfall a few months in advance, which is extremely beneficial for Australia’s agricultural industries.

A Comprehensive Review of Specific Yield in Land Surface and Groundwater Studies

AbstractSpecific yield is a key parameter for estimating water table depth in land surface models, which can strongly modulate the interaction between soil moisture and groundwater and further affect the water budget between the land surface and the atmosphere. The error in water table simulation comes mainly from the uncertainty in determining the specific yield. The determination of specific yield in land surface models is simplified, and a uniform empirical constant value of specific yield is generally used in global simulations, especially for deep aquifers, for its convenience. However, a uniform fixed value is insufficient for worldwide aquifers with diverse environments. Before estimating specific yields for global aquifers in Earth system simulations, a comprehensive understanding of the specific yield and comparison of current determination methods are required. This study provides a comprehensive review of the literature focusing on different concepts of the specific yield, complex variabilities, and various determination methods. Specifically, we detail the distinction and interconversion of different subconcepts that are used to reflect the influence of different factors on the specific yield and are likely to confuse scholars; present the representative comparisons of previous estimated specific yields using different methods, and summarize the applicabilities and limitations of these methods, indicating directions for their future improvement.

A digital twin of Earth for the green transition

For its green transition, the EU plans to fund the development of digital twins of Earth. For these twins to be more than big data atlases, they must create a qualitatively new Earth system simulation and observation capability using a methodological framework responsible for exceptional advances in numerical weather prediction.