Abstract. Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset. We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages). We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), https://doi.org/10.5281/zenodo.3998463 (Iturbide et al., 2020).

[1] A sudden reduction in rainfall occurred in the southwest of Western Australia in the mid-20th century. This reduced inflows to the Perth water supply by about 120 GL (42%) and led to an acceleration of projects to develop new water sources at a cost of about $300 million. The reduction in rainfall was coincident with warmer temperatures. A major analysis of these changes indicated that the changes in temperature were likely caused by the enhanced greenhouse effect and that the changes in rainfall were likely caused by a large-scale reorganization of the atmospheric circulation. We explore an alternative hypothesis that large-scale land cover change explains the observed changes in rainfall and temperature. We use three high-resolution mesoscale model configurations forced at the boundaries to simulate (for each model) five July climates for each of natural and current land cover. We find that land cover change explains up to 50% of the observed warming. Following land cover change, we also find, in every simulation, a reduction in rainfall over southwest Western Australia and an increase in rainfall inland that matches the observations well. We show that the reduced surface roughness following land cover change largely explains the simulated changes in rainfall by increasing moisture divergence over southwest Western Australia and increasing moisture convergence inland. Increased horizontal wind magnitudes and suppressed vertical velocities over southwest Western Australia reduce the likelihood of precipitation. Inland, moisture convergence and increased vertical velocities lead to an increase in rainfall. Our results indicate that rainfall over southwest Western Australia may be returned to the long-term average through large-scale reforestation, a policy option within the control of local government. Such a program would also provide a century-scale carbon sink to ameliorate Australia's very high per capita greenhouse gas emissions.

This paper examines the projected changes in rainfall in Southeast Asia (SEA) in the twenty-first century based on the multimodel simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA). A total of 11 General Circulation Models (GCMs) have been downscaled using 7 Regional Climate Models (RCMs) to a resolution of 25 km × 25 km over the SEA domain (89.5° E-146.5° E, 14.8° S-27.0° N) for two different representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5. The 1976-2005 period is considered as the historical period for evaluating the changes in seasonal precipitation of December-January-February (DJF) and June-July-August (JJA) over future periods of the early (2011-2040), mid (2041-2070) and late twenty-first century (2071-2099). The ensemble mean shows a good reproduction of the SEA climatological mean spatial precipitation pattern with systematic wet biases, which originated largely from simulations using the RegCM4 model. Increases in mean rainfall (10-20%) are projected throughout the twenty-first century over Indochina and eastern Philippines during DJF while a drying tendency prevails over the Maritime Continent. For JJA, projections of both RCPs indicate reductions in mean rainfall (10-30%) over the Maritime Continent, particularly over the Indonesian region by mid and late twenty-first century. However, examination of individual member responses shows prominent inter-model variations, reflecting uncertainty in the projections.

We investigated the performance of RegCM4 in simulating rainfall over Southeast Asia with different combinations of deep-convection and air−sea flux parameterization schemes. Four different gridded rainfall datasets were used for the model assessment. In general, the simulations produced dry biases over the equatorial region and slightly wet biases over mainland Indo-China, except those experiments with the MIT Emanuel cumulus schemes, in which large positive rainfall biases were simulated. However, simulations with the MIT schemes were generally better at reproducing annual rainfall variations. The simulations were not sensitive to the treatment of air−sea fluxes. While the simulations generally produced the rainfall climatology well, all simulations showed stronger inter-annual variability compared to observations. Nevertheless, the time evolution of the inter-annual variations was well reproduced, particularly over the eastern Maritime Continent. Over mainland Southeast Asia, all simulations produced unrealistic rainfall anomaly responses to surface temperature. The lack of summer air−sea interactions in the model resulted in enhanced oceanic forcing over the regions, leading to positive rainfall anomalies during years with warm ocean temperature anomalies. This shortcoming in turn caused much stronger atmospheric forcing on the land surface processes compared to that of the observation. A robust score-ranking system was designed to rank the simulations according to their performance in reproducing different aspects of rainfall characteristics. The results suggest that the simulation with the MIT Emanuel convective scheme and the BATS1e air−sea flux scheme performs better overall compared to the rest of the simulations.

We explore the impact of future climate change on the risk of forest and grassland fires over Australia in January using a high resolution regional climate model, driven at the boundaries by data from a transitory coupled climate model. Two future emission scenarios (relatively high and relatively low) are used for 2050 and 2100 and four realizations for each time period and each emission scenario are run. Results show a consistent increase in regional-scale fire risk over Australia driven principally by warming and reductions in relative humidity in all simulations, under all emission scenarios and at all time periods. We calculate the probability density function for the fire risk for a single point in New South Wales and show that the probability of extreme fire risk increases by around 25% compared to the present day in 2050 under both relatively low and relatively high emissions, and that this increases by a further 20% under the relatively low emission scenario by 2100. The increase in the probability of extreme fire risk increases dramatically under the high emission scenario by 2100. Our results are broadly in-line with earlier analyses despite our use of a significantly different methodology and we therefore conclude that the likelihood of a significant increase in fire risk over Australia resulting from climate change is very high. While there is already substantial investment in fire-related management in Australia, our results indicate that this investment is likely to have to increase to maintain the present fire-related losses in Australia.

In this study, simulations over Southeast Asia (15°S–40°N, 80°–145°E) at 36 km resolution were conducted for the period 1989–2007 using the Regional Climate Model version 4.3 (RegCM4.3) under the framework of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment – Southeast Asia (or SEACLID/CORDEX‐SEA) project. Forced by the European Centre for Medium‐Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA‐Interim), 18 experiments were carried out using different combinations of cumulus parameterization and ocean flux schemes. Twelve extreme indices for both rainfall and temperature were estimated from the model output. A statistical omega index was used to measure the degree of similarity among the 18 experiments in phase and shape. The results showed relatively high similarities among the experiments over mainland Asia compared to those over the Maritime Continent for both seasonal and inter‐annual variability. The extreme rainfall indices had a lower omega compared to that of temperature. Observed daily rainfall and temperature data at 52 meteorological stations over the SEA region were used to validate the simulated extreme indices. The results showed that extreme temperature indices were generally underestimated across the region. Systematic biases for each simulated rainfall index were also identified. A score ranking system was established to compare the relative performance of the 18 experiments over the 52 selected stations objectively. It was shown that the experiments with the Massachusetts Institute of Technology (MIT)‐Emanuel scheme performed relatively better than the other convective schemes. The combination of the MIT‐Emanuel convective scheme with the Biosphere–Atmosphere Transfer scheme (BATS1e) ocean flux scheme produced the best performance.

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Ecosystems provide multiple benefits to people, including climate regulation. Previous efforts to quantify this ecosystem service have been either largely conceptual or based on complex atmospheric models. Here, we review previous research on this topic and propose a new and simple analytical approach for estimating the physical regulation of climate by ecosystems. The proposed metric estimates how land‐cover change affects the loading of heat and moisture into the atmosphere, while also accounting for the relative contribution of wind‐transported heat and moisture. Although feedback dynamics between land, atmosphere, and oceans are not modeled, the metric compares well with previous studies for several regions. We find that ecosystems have the strongest influence on surface climatic conditions in the boreal and tropical regions, where temperature and moisture changes could substantially offset or magnify greenhouse‐forced changes. This approach can be extended to estimate the effects of changing land cover on local, physical climate processes that are relevant to society.