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Land-use and land-cover changes (hereafter simply “land use”) alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model-based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (tLU) using regularized optimal fingerprinting for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside historical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved continental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimodel mean. Furthermore, using a principal component analysis, we find that tLU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use.

A new regional climate projection ensemble has been created for the Australasia region as part of the World Climate Research Programs Coordinated Regional Downscaling Experiment (CORDEX). The CORDEX-Australasia ensemble is the largest regional climate projection ensemble ever created for the region. It is a 20-member ensemble made by 6 regional climate models downscaling 11 global climate models. Overall the ensemble produces a good representation of recent climate. Consistent biases within the ensemble include an underestimation of the diurnal temperature range and an underestimation of precipitation across much of southern Australia. Under a high emissions scenario projected temperature changes by the end of the twenty-first century reach ~ 5 K in the interior of Australia with smaller increases found toward the coast. Projected precipitation changes are towards drying, particularly in the most populated areas of the southwest and southeast of the continent. The projected precipitation change is very seasonal with summer projected to see little change leaning toward an increase. These results provide a foundation enabling future studies of regional climate changes, climate change impacts, and adaptation options for Australia.

Understanding and quantifying land management impacts on local climate is important for distinguishing between the effects of land management and large-scale climate forcings. This study for the first time explicitly considers the radiative forcing resulting from realistic land management and offers new insights into the local land surface response to land management. Regression-based trend analysis is applied to observations and present-day ensemble simulations with the Community Earth System Model (CESM) version 1.2.2 to assess the impact of irrigation and conservation agriculture (CA) on warming trends using an approach that is less sensitive to temperature extremes. At the regional scale, an irrigation- and CA-induced acceleration of the annual mean near-surface air temperature (T2m) warming trends and the annual maximum daytime temperature (TXx) warming trends were evident. Estimation of the impact of irrigation and CA on the spatial average of the warming trends indicated that irrigation and CA have a pulse cooling effect on T2m and TXx, after which the warming trends increase at a greater rate than the control simulations. This differed at the local (subgrid) scale under irrigation where surface temperature cooling and the dampening of warming trends were both evident. As the local surface warming trends, in contrast to regional trends, do not account for atmospheric (water vapour) feedbacks, their dampening confirms the importance of atmospheric feedbacks (water vapour forcing) in explaining the enhanced regional trends. At the land surface, the positive radiative forcing signal arising from enhanced atmospheric water vapour is too weak to offset the local cooling from the irrigation-induced increase in the evaporative fraction. Our results underline that agricultural management has complex and non-negligible impacts on the local climate and highlight the need to evaluate the representation of land management in global climate models using climate models of higher resolution., Earth System Dynamics, 13 (1), ISSN:2190-4987, ISSN:2190-4979

The Community Atmosphere Biosphere Land Exchange (CABLE) model is a third‐generation land surface model (LSM). CABLE is commonly used as a stand‐alone LSM, coupled to the Australian Community Climate and Earth Systems Simulator global climate model and coupled to the Weather Research and Forecasting (WRF) model for regional applications. Here, we evaluate an updated version of CABLE within a WRF physics ensemble over the COordinated Regional Downscaling EXperiment (CORDEX) AustralAsia domain. The ensemble consists of different cumulus, radiation and planetary boundary layer (PBL) schemes. Simulations are carried out within the NASA Unified WRF modeling framework, NU‐WRF. Our analysis did not identify one configuration that consistently performed the best for all diagnostics and regions. Of the cumulus parameterizations the Grell‐Freitas cumulus scheme consistently overpredicted precipitation, while the new Tiedtke scheme was the best in simulating the timing of precipitation events. For the radiation schemes, the RRTMG radiation scheme had a general warm bias. For the PBL schemes, the YSU scheme had a warm bias, and the MYJ PBL scheme a cool bias. Results are strongly dependent on the region of interest, with the northern tropics and southwest Western Australia being more sensitive to the choice of physics options compared to southeastern Australia which showed less overall variation and overall better performance across the ensemble. Comparisons with simulations using the Unified Noah LSM showed that CABLE in NU‐WRF has a more realistic simulation of evapotranspiration when compared to GLEAM estimates.

The World Climate Research Programme (WCRP) has an international initiative called the COordinated Regional climate Downscaling EXperiment (CORDEX). The goal of the initiative is to provide regionally downscaled climate projections for most land regions of the globe, as a compliment to the global climate model projections performed within the Coupled Model Intercomparison Projects (CMIP). CORDEX includes data from both dynamical and statistical downscaling. It is anticipated that the CORDEX dataset will provide a link to the impacts and adaptation community through its better resolution and regional focus. Participation in CORDEX is open and any researchers performing climate downscaling are encourage to engage with the initiative. Here I present the current status, evaluation and future projections for the CORDEX-AustralAsia ensemble.The CORDEX-Australasia ensemble is the largest regional climate projection ensemble ever created for the region. It is a 20-member ensemble made by 6 regional climate models downscaling 11 global climate models. Overall the ensemble produces a good representation of recent climate. Consistent biases within the ensemble include an underestimation of the diurnal temperature range and an underestimation of precipitation across much of southern Australia. Under a high emissions scenario projected temperature changes by the end of the twenty-first century reach ~ 5 K in the interior of Australia with smaller increases found toward the coast. Projected precipitation changes are towards drying, particularly in the most populated areas of the southwest and southeast of the continent. The projected precipitation change is very seasonal with summer projected to see little change leaning toward an increase. These results provide a foundation enabling future studies of regional climate changes, climate change impacts, and adaptation options for Australia.

Climate observations and projections for Australia show an increase in warm temperature extremes, including the frequency, duration and intensity of heatwaves. Recent global scale studies have suggested that agricultural land-use management options, such as increasing crop albedo, could reducing local warming. Australia has approximately 3,727,210 km2 of cropland agricultural land-use, the majority of which is in southwest Western Australia and southeast Australia. This presents a potential opportunity to reduce regional warming via crop albedo enhancement. We use a regional climate model at 10 km resolution, to show that crop albedo enhancement of up to 0.1 could reduce monthly mean daily maximum temperatures by −1.0 °C to −1.2 °C, and monthly highest maximum temperatures by up to −1.4 °C to −1.6 °C during the cropping season. This cooling is approximately 3 times higher over Australia than global climate models predict. We highlight stronger cooling over southwest Western Australia as compared to southeast Australia, the opposite to global model studies which poorly resolve southwestern agricultural regions. The regional cooling was driven by a reduction in surface net shortwave radiation leading to a decrease in both sensible and latent heat flux of up to 50 W m−2 and 20 W m−2 respectively, when albedo is increased by up to 0.1. There were no cloud feedbacks or effects on precipitation. Our results highlight the importance of using regional climate models at a sufficiently high spatial resolution when investigating agricultural land-use management to reduce regional warming.

Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs., Compound climate events such as floods and droughts together can cause severe socio-economic impacts. Here, the authors analyse global hazard pairs from 1980–2014 and find global hotspots for the occurrence of compound events.

Irrigation affects climate conditions – and especially hot extremes – in various regions across the globe. Yet how these climatic effects compare to other anthropogenic forcings is largely unknown. Here we provide observational and model evidence that expanding irrigation has dampened historical anthropogenic warming during hot days, with particularly strong effects over South Asia. We show that irrigation expansion can explain the negative correlation between global observed changes in daytime summer temperatures and present-day irrigation extent. While global warming increases the likelihood of hot extremes almost globally, irrigation can regionally cancel or even reverse the effects of all other forcings combined. Around one billion people (0.79–1.29) currently benefit from this dampened increase in hot extremes because irrigation massively expanded throughout the 20\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{th}$$\end{document}th century. Our results therefore highlight that irrigation substantially reduced human exposure to warming of hot extremes but question whether this benefit will continue towards the future., How the effects of irrigation on the climate conditions compare to other anthropogenic forcings is not well known. Observational and model evidence show that expanding irrigation has dampened historical anthropogenic warming during hot days, an effect that is particularly strong over South Asia.

Including the parameterization of land management practices into Earth System Models has been shown to influence the simulation of regional climates, particularly for temperature extremes. However, recent model development has focused on implementing irrigation where other land management practices such as conservation agriculture (CA) has been limited due to the lack of global spatially explicit datasets describing where this form of management is practiced. Here, we implement a representation of CA into the Community Earth System Model and show that the quality of simulated surface energy fluxes improves when including more information on how agricultural land is managed. We also compare the climate response at the subgrid scale where CA is applied. We find that CA generally contributes to local cooling (~1°C) of hot temperature extremes in mid-latitude regions where it is practiced, while over tropical locations CA contributes to local warming (~1°C) due to changes in evapotranspiration dominating the effects of enhanced surface albedo. In particular, changes in the partitioning of evapotranspiration between soil evaporation and transpiration are critical for the sign of the temperature change: a cooling occurs only when the soil moisture retention and associated enhanced transpiration is sufficient to offset the warming from reduced soil evaporation. Finally, we examine the climate change mitigation potential of CA by comparing a simulation with present-day CA extent to a simulation where CA is expanded to all suitable crop areas. Here, our results indicate that while the local temperature response to CA is considerable cooling (>2°C), the grid-scale changes in climate are counteractive due to negative atmospheric feedbacks. Overall, our results underline that CA has a nonnegligible impact on the local climate and that it should therefore be considered in future climate projections. ispartof: GLOBAL CHANGE BIOLOGY vol:24 issue:10 pages:4758-4774 ispartof: location:England status: published

Greenhouse gas emissions urgently need to be reduced. Even with a step up in mitigation, the goal of limiting global temperature rise to well below 2 °C remains challenging. Consequences of missing these goals are substantial, especially on regional scales. Because progress in the reduction of carbon dioxide emissions has been slow, climate engineering schemes are increasingly being discussed. But global schemes remain controversial and have important shortcomings. A reduction of global mean temperature through global-scale management of solar radiation could lead to strong regional disparities and affect rainfall patterns. On the other hand, active management of land radiative effects on a regional scale represents an alternative option of climate engineering that has been little discussed. Regional land radiative management could help to counteract warming, in particular hot extremes in densely populated and important agricultural regions. Regional land radiative management also raises some ethical issues, and its efficacy would be limited in time and space, depending on crop growing periods and constraints on agricultural management. But through its more regional focus and reliance on tested techniques, regional land radiative management avoids some of the main shortcomings associated with global radiation management. We argue that albedo-related climate benefits of land management should be considered more prominently when assessing regional-scale climate adaptation and mitigation as well as ecosystem services.

Heatwaves have implications for human health and ecosystem function. Over cities, the impacts of a heatwave event may be compounded by urban heat, where temperatures over the urban area are higher than their rural surroundings. Coastal cities often rely upon sea breezes to provide temporary relief. However, topographic features contributing to the development of Foehn-like conditions can offset the cooling influence of sea breezes. Using convection-permitting simulations (⩽4 km) we examine the potential for both mechanisms to influence heatwave conditions over the large coastal city of Sydney, Australia that is bordered by mountains. Heatwave onset in the hot period of January–February 2017 often coincides with a hot continental flow over the mountains into the city. The temperature difference between the coast and the urban–rural interface can reach 15.79 °C. Further, the urban heat island contributes on average an additional 1 °C in the lowest 1 km of the atmosphere and this often extends beyond the city limits. The cumulative heat induced by the urban environment reaches 10 °C over the city and 3 °C over adjacent inland areas. Strong sea breezes are important for heat dispersion with city temperature gradients reducing to within 1 °C. The resolution permits a comparison between urban types and reveals that the diurnal cycle of temperature, moisture content and wind are sensitive to the urban type. Here we show that convection permitting simulations can resolve the interaction between local breezes and the urban environment that are not currently resolved in coarser resolution models.

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20502-8.

This paper presents a methodology for examining land–atmosphere coupling in a regional climate model by examining how the resistances to moisture transfer from the land to the atmosphere control the surface turbulent energy fluxes. Perturbations were applied individually to the aerodynamic resistance from the soil surface to the displacement height, the aerodynamic resistance from the displacement height to the reference level, the stomatal resistance, and the leaf boundary layer resistance. Only perturbations to the aerodynamic resistance from the soil surface to the displacement height systematically affected 2-m air temperature for the shrub and evergreen boreal forest plant functional types (PFTs). This was associated with this resistance systematically increasing the terrestrial and atmospheric components of the land–atmosphere coupling strength through changes in the partitioning of the surface energy balance. Perturbing the other resistances did contribute to changing the partitioning of the surface energy balance but did not lead to systematic changes in the 2-m air temperature. The results suggest that land–atmosphere coupling in the modeling system presented here acts mostly through the aerodynamic resistance from the soil surface to the displacement height, which is a function of both the friction velocity and vegetation height and cover. The results show that a resistance pathway framework can be used to examine how changes in the resistances affect the partitioning of the surface energy balance and how this subsequently influences surface climate through land–atmosphere coupling. Limitations in the present analysis include grid-scale rather than PFT-scale analysis, the exclusion of resistance dependencies, and the linearity assumption of how temperature responds to a resistance perturbation.

The impacts of land use have been shown to have considerable influence on regional climate. With the recent international commitment to limit global warming to well below 2 degrees C, emission reductions need to be ambitious and could involve major land-use change (LUC). Land-based mitigation efforts to curb emissions growth include increasing terrestrial carbon sequestration through reforestation, or the adoption of bioenergy crops. These activities influence local climate through biogeophysical feedbacks, however, it is uncertain how important they are for a 1.5 degrees climate target. This was the motivation for HAPPI-Land: the half a degree additional warming, prognosis, and projected impactsland-use scenario experiment. Using four Earth system models, we present the first multimodel results from HAPPI-Land and demonstrate the critical role of land use for understanding the characteristics of regional climate extremes in low-emission scenarios. In particular, our results show that changes in temperature extremes due to LUC are comparable in magnitude to changes arising from half a degree of global warming. We also demonstrate that LUC contributes to more than 20% of the change in temperature extremes for large land areas concentrated over the Northern Hemisphere. However, we also identify sources of uncertainty that influence the multimodel consensus of our results including how LUC is implemented and the corresponding biogeophysical feedbacks that perturb climate. Therefore, our results highlight the urgent need to resolve the challenges in implementing LUC across models to quantify the impacts and consider how LUC contributes to regional changes in extremes associated with sustainable development pathways. Plain Language Summary The motivation for the Intergovernmental Panel on Climate Change Special Report of 1.5 degrees C stems from the need to understand how the impacts of climate change may evolve for half a degree of global warming. Most low-emission scenarios involve substantial land-use change (LUC) including the expansion of bioenergy and food crops, as well as afforestation. Future emission scenarios used as input to climate models are derived using integrated assessment models, and focus on greenhouse gas emissions. However, changes in land use also have a direct effect on local climate through the local water and energy balances, which is not considered in these models, and therefore, our understanding on how dependent these climate projections are to the choice of land-use scenario is limited. Our study demonstrates that the land-use scenario has a considerable influence on the projections of temperature extremes for low-emission scenarios. In particular, for large land areas in the Northern Hemisphere, more than 20% of the change in temperature extremes can be attributed to LUC. However, our study also reveals that considerable uncertainty remains on what the feedbacks of land use may mean for land-based mitigation activities.

Conservation agriculture (CA) is widely promoted as a sustainable agricultural management strategy with the potential to alleviate some of the adverse effects of modern, industrial agriculture such as large‐scale soil erosion, nutrient leaching and overexploitation of water resources. Moreover, agricultural land managed under CA is proposed to contribute to climate change mitigation and adaptation through reduced emission of greenhouse gases, increased solar radiation reflection, and the sustainable use of soil and water resources. Due to the lack of official reporting schemes, the amount of agricultural land managed under CA systems is uncertain and spatially explicit information about the distribution of CA required for various modeling studies is missing. Here, we present an approach to downscale present‐day national‐level estimates of CA to a 5 arcminute regular grid, based on multicriteria analysis. We provide a best estimate of CA distribution and an uncertainty range in the form of a low and high estimate of CA distribution, reflecting the inconsistency in CA definitions. We also design two scenarios of the potential future development of CA combining present‐day data and an assessment of the potential for implementation using biophysical and socioeconomic factors. By our estimates, 122–215 Mha or 9%–15% of global arable land is currently managed under CA systems. The lower end of the range represents CA as an integrated system of permanent no‐tillage, crop residue management and crop rotations, while the high estimate includes a wider range of areas primarily devoted to temporary no‐tillage or reduced tillage operations. Our scenario analysis suggests a future potential of CA in the range of 533–1130 Mha (38%–81% of global arable land). Our estimates can be used in various ecosystem modeling applications and are expected to help identifying more realistic climate mitigation and adaptation potentials of agricultural practices. ISSN:1354-1013 ISSN:1365-2486

Land–atmosphere coupling can strongly affect climate and climate extremes. Estimates of land–atmosphere coupling vary considerably between climate models, between different measures used to define coupling, and between the present and the future. The Australian Community Climate and Earth-System Simulator, version 1.3b (ACCESS1.3b), is used to derive and examine previously used measures of coupling strength. These include the GLACE-1 coupling measure derived on seasonal time scales; a similar measure defined using multiyear simulations; and four other measures of different complexity and data requirements, including measures that can be derived from standard model runs and observations. The ACCESS1.3b land–atmosphere coupling strength is comparable to other climate models. The coupling strength in the Southern Hemisphere summer is larger compared to the Northern Hemisphere summer and is dominated by a strong signal in the tropics and subtropics. The land–atmosphere coupling measures agree on the location of very strong land–atmosphere coupling but show differences in the spatial extent of these regions. However, the investigated measures show disagreement in weaker coupled regions, and some regions are only identified by a single measure as strongly coupled. In future projections the soil moisture trend is crucial in generating regions of strong land–atmosphere coupling, and the results suggest an expansion of coupling “hot spots.” It is concluded that great care needs to be taken in using different measures of coupling strength and shown that several measures that can be easily derived lead to inconsistent conclusions with more computationally expensive measures designed to measure coupling strength.

The role of land–atmosphere coupling in modulating the impact of land-use change (LUC) on regional climate extremes remains uncertain. Using the Weather and Research Forecasting Model, this study combines the Global Land–Atmosphere Coupling Experiment with regional LUC to assess the combined impact of land–atmosphere coupling and LUC on simulated temperature extremes. The experiment is applied to an ensemble of planetary boundary layer (PBL) and cumulus parameterizations to determine the sensitivity of the results to model physics. Results show a consistent weakening in the soil moisture–maximum temperature coupling strength with LUC irrespective of the model physics. In contrast, temperature extremes show an asymmetric response to LUC dependent on the choice of PBL scheme, which is linked to differences in the parameterization of vertical transport. This influences convective precipitation, contributing a positive feedback on soil moisture and consequently on the partitioning of the surface turbulent fluxes. The results suggest that the impact of LUC on temperature extremes depends on the land–atmosphere coupling that in turn depends on the choice of PBL. Indeed, the sign of the temperature change in hot extremes resulting from LUC can be changed simply by altering the choice of PBL. The authors also note concerns over the metrics used to measure coupling strength that reflect changes in variance but may not respond to LUC-type perturbations.

Limiting global warming to well below 2°C is an imminent challenge for humanity. However, even if this global target can be met, some regions are still likely to experience substantial warming relative to others. Using idealized global climate simulations, we examine the potential of land management options in affecting regional climate, with a focus on crop albedo enhancement and irrigation (climate-effective land management). The implementation is performed over all crop regions globally to provide an upper bound. We find that the implementation of both crop albedo enhancement and irrigation can reduce hot temperature extremes by more than 2°C in North America, Eurasia, and India over the 21st century relative to a scenario without management application. The efficacy of crop albedo enhancement scales with the magnitude, where a cooling response exceeding 0.5°C for hot temperature extremes was achieved with a large (i.e., ≥0.08) change in crop albedo. Regional differences were attributed to the surface energy balance response with temperature changes mostly explained by latent heat flux changes for irrigation and net shortwave radiation changes for crop albedo enhancement. However, limitations do exist, where we identify warming over the winter months when climate-effective land management is temporarily suspended. This was associated with persistent cloud cover that enhances longwave warming. It cannot be confirmed if the magnitude of this feedback is reproducible in other climate models. Our results overall demonstrate that regional warming of hot extremes in our climate model can be partially mitigated when using an idealized treatment of climate-effective land management.

Irrigation is an essential practice for sustaining global food production and many regional economies. Emerging scientific evidence indicates that irrigation substantially affects mean climate conditions in different regions of the world. Yet how this practice influences climate extremes is currently unknown. Here we use ensemble simulations with the Community Earth System Model to assess the impacts of irrigation on climate extremes. An evaluation of the model performance reveals that irrigation has a small yet overall beneficial effect on the representation of present-day near-surface climate. While the influence of irrigation on annual mean temperatures is limited, we find a large impact on temperature extremes, with a particularly strong cooling during the hottest day of the year (−0.78 K averaged over irrigated land). The strong influence on extremes stems from the timing of irrigation and its influence on land-atmosphere coupling strength. Together these effects result in asymmetric temperature responses, with a more pronounced cooling during hot and/or dry periods. The influence of irrigation is even more pronounced when considering subgrid-scale model output, suggesting that local effects of land management are far more important than previously thought. Our results underline that irrigation has substantially reduced our exposure to hot temperature extremes in the past and highlight the need to account for irrigation in future climate projections.

The severity of recent droughts and heat waves have been linked to land-atmosphere feedbacks. However, investigations of how these feedbacks are influenced by land use and land cover change (LULCC) are limited. Using the Weather and Research Forecasting model with an ensemble framework of planetary boundary layer and cumulus parameterization schemes, we combine the Global Land Atmosphere Coupling Experiment methodology with LULCC to assess how LULCC affects land-atmosphere coupling strength for maximum temperature over Australia. We find a statistically significant decrease in soil moisture-temperature coupling over regions where forest changes to crops, which was consistent across the implemented model physics and background climate. This was associated with a decrease in the ensemble mean variance suggesting that LULCC influences regional climate variability via changes in the regional scale hydrology and surface energy balance. Our results highlight the need to consider land surface changes and coupling strength in combination, rather than in isolation.

Climate extremes, such as heat waves and heavy precipitation events, have large impacts on ecosystems and societies. Climate models provide useful tools for studying underlying processes and amplifying effects associated with extremes. The Australian Community Climate and Earth System Simulator (ACCESS) has recently been coupled to the Community Atmosphere Biosphere Land Exchange (CABLE) model. We examine how this model represents climate extremes derived by the Expert Team on Climate Change Detection and Indices (ETCCDI) and compare them to observational data sets using the AMIP framework. We find that the patterns of extreme indices are generally well represented. Indices based on percentiles are particularly well represented and capture the trends over the last 60 years shown by the observations remarkably well. The diurnal temperature range is underestimated, minimum temperatures (TMIN) during nights are generally too warm and daily maximum temperatures (TMAX) too low in the model. The number of consecutive wet days is overestimated, while consecutive dry days are underestimated. The maximum consecutive 1-day precipitation amount is underestimated on the global scale. Biases in TMIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in TMAX depend on biases in net shortwave radiation as well as evapotranspiration. The regions and season where the bias in evapotranspiration plays a role for the TMAX bias correspond to regions and seasons where soil moisture availability is limited. Our analysis provides the foundation for future experiments that will examine how land-surface processes contribute to these systematic biases in the ACCESS modelling system.

Using the Weather and Research Forecasting model we derive the first estimates for intraseasonal soil moisture-atmosphere coupling strength for the Australian summer climate using methodology adapted from the Global Land-Atmosphere Coupling Experiment. We examine the variations in coupling strength by perturbing the background climate (dry versus wet year) and the model physics (planetary boundary layer or cumulus scheme). For all choices of model physics, results identify Australia as a “hot spot” of soil moisture-atmosphere coupling for both mean and maximum temperatures. For the wet case, results are consistent for maximum temperature for all physics choices. Results diverge more for maximum temperature in the chosen dry year. The coupling of soil moisture with minimum temperature is weaker but consistent for all choices of model physics or whether a wet or dry year is used. Coupling strength for precipitation is weak and not statistically significant irrespective of the choice of model physics.

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

Accurate estimates of long-term linear trends of wind speed provide a useful indicator for circulation changes in the atmosphere and are invaluable for the planning and financing of sectors such as wind energy. Here a large number of wind observations over Australia and reanalysis products are analyzed to compute such trends. After a thorough quality control of the observations, it is found that the wind speed trends for 1975–2006 and 1989–2006 over Australia are sensitive to the height of the station: they are largely negative for the 2-m data but are predominantly positive for the 10-m data. The mean relative trend at 2 m is −0.10 ± 0.03% yr−1 (−0.36 ± 0.04% yr−1) for the 1975–2006 (1989–2006) period, whereas at 10 m it is 0.90 ± 0.03% yr−1 (0.69 ± 0.04% yr−1) for the 1975–2006 (1989–2006) period. Also, at 10 m light winds tend to increase more rapidly than the mean winds, whereas strong winds increase less rapidly than the mean winds; at 2 m the trends in both light and strong winds vary in line with the mean winds. It was found that a qualitative link could be established between the observed features in the linear trends and some atmospheric circulation indicators (mean sea level pressure, wind speed at 850 hPa, and geopotential at 850 hPa), particularly for the 10-m observations. Further, the magnitude of the trend is also sensitive to the period selected, being closer to zero when a very long period, 1948–2006, is considered. As a consequence, changes in the atmospheric circulation on climatic time scales appear unlikely.

Climate extremes, such as heat waves and heavy precipitation events, have large impacts on ecosystems and societies. Climate models provide useful tools to study underlying processes and amplifying effects associated with extremes. The Australian Community Climate and Earth System Simulator (ACCESS) has recently been coupled to the Community Atmosphere Biosphere Land Exchange model. We examine how this model represents climate extremes derived by the Expert Team on Climate Change Detection and Indices and compare them to observational datasets using the AMIP framework. We find that the patterns of extreme indices are generally well represented. Indices based on percentiles are particularly well represented and capture the trends over the last 60 yr shown by the observations remarkably well. The diurnal temperature range is underestimated, minimum temperatures (TMIN) during nights are generally too warm and daily maximum temperatures (TMAX) too low in the model. The number of consecutive wet days is overestimated while consecutive dry days are underestimated. The maximum consecutive 1 day precipitation amount is underestimated on the global scale. Biases in TMIN correlate well with biases in incoming longwave radiation, suggesting a relationship with biases in cloud cover. Biases in TMAX depend on biases in net shortwave radiation as well as evapotranspiration. The regions and season where the bias in evapotranspiration plays a role for the TMAX bias correspond to regions and seasons where soil moisture availability is limited. Our analysis provides the foundation for future experiments that will examine how land surface processes contribute to these systematic biases in the ACCESS modelling system.

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.