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Ailie J. E. Gallant and David J. Karoly

Abstract

Changes in the area of Australia experiencing concurrent temperature and rainfall extremes are investigated through the use of two combined indices. The indices describe variations between the fraction of land area experiencing extreme cold and dry or hot and wet conditions. There is a high level of agreement between the variations and trends of the indices from 1957 to 2008 when computed using (i) a spatially complete gridded dataset without rigorous quality control checks and (ii) spatially incomplete high-quality station datasets with rigorous quality control checks. Australian extremes are examined starting from 1911, which is the first time a broad-scale assessment of Australian temperature extremes has been performed prior to 1957. Over the whole country, the results show an increase in the extent of hot and wet extremes and a decrease in the extent of cold and dry extremes annually and during all seasons from 1911 to 2008 at a rate of between 1% and 2% decade−1. These trends mostly stem from changes in tropical regions during summer and spring. There are relationships between the extent of extreme maximum temperatures, precipitation, and soil moisture on interannual and decadal time scales that are similar to the relationships exhibited by variations of the means. However, the trends from 1911 to 2008 and from 1957 to 2008 are not consistent with these relationships, providing evidence that the processes causing the interannual variations and those causing the longer-term trends are different.

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Stephanie J. Jacobs, Ailie J. E. Gallant, and Nigel J. Tapper

Abstract

The sensitivity of near-surface urban meteorological conditions to three different soil moisture initialization experiments under heat-wave conditions is investigated for the city of Melbourne, Australia. The Weather Research and Forecasting Model is used to simulate a domain over Melbourne and its surrounding rural areas. The experiments employ three suites of simulations. Two suites initialize the model with soil moisture from the top layer of the ERA-Interim soil moisture data with a 3-month and 24-h coupled spinup period, respectively. The third suite initializes the model with the arguably more realistic soil moistures from the Australian Water Availability Project (AWAP), which are an order of magnitude drier than the ERA-Interim data, again using a 24-h spinup period. The simulations employing the AWAP data are found to have smaller errors when compared with observations, with biases in urban maximum temperature reduced by 4.1°C and biases in the skin temperature reduced by 3.0°C relative to the biases of the 3-month-spinup experiment. Despite urban areas only having a small proportion of soil-covered surfaces, the results show that urban soils have a greater influence on urban near-surface temperatures at night, whereas rural soils have a greater influence on urban near-surface temperatures during the daytime.

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Ailie J. E. Gallant, David J. Karoly, and Karin L. Gleason

Abstract

The utility of a combined modified climate extremes index (mCEI) is presented for monitoring coherent trends in multiple types of climate extremes across large regions. Its usefulness lies in its ability to distill complex spatiotemporal fields into a simple, flexible nonparametric index.

Two versions of the mCEI are computed that incorporate changes in several annual- or daily-scale temperature-related and moisture-related extremes. Applying data from the contiguous United States, Europe, and Australia detects consistent and statistically significant increases in the spatial prevalence of climate extremes from 1950 to 2012. All three continental-scale regions show increasingly widespread warm annual- and daily-scale minimum and maximum temperature extremes, a decreasing spatial extent of cool annual- and daily-scale minimum and maximum temperature extremes, and increasing areas where the proportion of annual total precipitation falls on heavy-rain days. There were no statistically significant trends toward more widespread, annual-scale drought or moisture surplus in any region.

The dependence of annual extremes on the frequency of daily-scale extremes is highlighted by the strong covariations between annual- and daily-scale extremes in all regions. By the nature of construction of the combined indices, the differences in the trends of the mCEI and daily-scale mCEI (dmCEI) suggest that extremes in more areas are changing primarily because of a shift of temperature and daily rainfall distributions toward warm extremes and heavy-rainfall extremes.

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David Hoffmann, Ailie J. E. Gallant, and Mike Hobbins

Abstract

“Flash drought” (FD) describes the rapid onset of drought on subseasonal times scales. It is of particular interest for agriculture because it can deplete soil moisture for crop growth in just a few weeks. To better understand the processes causing FD, we evaluate the importance of evaporative demand and precipitation by comparing three different drought indices that estimate this hazard using meteorological and hydrological parameters from the CMIP5 suite of models. We apply the standardized precipitation index (SPI); the evaporative demand drought index (EDDI), derived from evaporative demand E 0; and the evaporative stress index (ESI), which connects atmospheric and soil moisture conditions by measuring the ratio of actual and potential evaporation. The results show moderate-to-strong relationships (r 2 > 0.5) between drought indices and upper-level soil moisture on daily time scales, especially in drought-prone regions. We find that all indices are able to identify FD in the top 10-cm layer of soil moisture in a similar proportion to that in the models’ climatologies. However, there is significant intermodel spread in the characteristics of the FDs identified. This spread is mainly caused by an overestimation of E 0, indicating stark differences in the land surface models and coupling in individual CMIP5 models. Of all indices, the SPI provides the highest skill in predicting FD prior to or at the time of onset in soil moisture, while both EDDI and ESI show significantly lower skill. The results highlight that the lack of precipitation is the main contributor to FDs in climate models, with E 0 playing a secondary role.

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Ailie J. E. Gallant, Steven J. Phipps, David J. Karoly, A. Brett Mullan, and Andrew M. Lorrey

Abstract

The stationarity of relationships between local and remote climates is a necessary, yet implicit, assumption underlying many paleoclimate reconstructions. However, the assumption is tenuous for many seasonal relationships between interannual variations in the El Niño–Southern Oscillation (ENSO) and the southern annular mode (SAM) and Australasian precipitation and mean temperatures. Nonstationary statistical relationships between local and remote climates on the 31–71-yr time scale, defined as a change in their strength and/or phase outside that expected from local climate noise, are detected on near-centennial time scales from instrumental data, climate model simulations, and paleoclimate proxies.

The relationships between ENSO and SAM and Australasian precipitation were nonstationary at 21%–37% of Australasian stations from 1900 to 2009 and strongly covaried, suggesting common modulation. Control simulations from three coupled climate models produce ENSO-like and SAM-like patterns of variability, but differ in detail to the observed patterns in Australasia. However, the model teleconnections also display nonstationarity, in some cases for over 50% of the domain. Therefore, nonstationary local–remote climatic relationships are inherent in environments regulated by internal variability. The assessments using paleoclimate reconstructions are not robust because of extraneous noise associated with the paleoclimate proxies.

Instrumental records provide the only means of calibrating and evaluating regional paleoclimate reconstructions. However, the length of Australasian instrumental observations may be too short to capture the near-centennial-scale variations in local–remote climatic relationships, potentially compromising these reconstructions. The uncertainty surrounding nonstationary teleconnections must be acknowledged and quantified. This should include interpreting nonstationarities in paleoclimate reconstructions using physically based frameworks.

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Stephanie J. Jacobs, Ailie J. E. Gallant, Nigel J. Tapper, and Dan Li

Abstract

The ability of cool roofs and vegetation to reduce urban temperatures and improve human thermal stress during heat wave conditions is investigated for the city of Melbourne, Australia. The Weather Research and Forecasting Model coupled to the Princeton Urban Canopy Model is employed to simulate 11 scenarios of cool roof uptake across the city, increased vegetation cover across the city, and a combination of these strategies. Cool roofs reduce urban temperatures during the day, and, if they are installed across enough rooftops, their cooling effect extends to the night. In contrast, increasing vegetation coverage reduces nighttime temperatures but results in minimal cooling during the hottest part of the day. The combination of cool roofs and increased vegetation scenarios creates the largest reduction in temperature throughout the heat wave, although the relationship between the combination scenarios is nonsynergistic. This means that the cooling occurring from the combination of both strategies is either larger or smaller than if the cooling from individual strategies were to be added together. The drier, lower-density western suburbs of Melbourne showed a greater cooling response to increased vegetation without enhancing human thermal stress due to the corresponding increase in humidity. The leafy medium-density eastern suburbs of Melbourne showed a greater cooling response to the installation of cool roofs. These results highlight that the optimal urban cooling strategies can be different across a single urban center.

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Joëlle Gergis, Raphael Neukom, Ailie J. E. Gallant, and David J. Karoly

Abstract

Multiproxy warm season (September–February) temperature reconstructions are presented for the combined land–ocean region of Australasia (0°–50°S, 110°E–180°) covering 1000–2001. Using between 2 (R2) and 28 (R28) paleoclimate records, four 1000-member ensemble reconstructions of regional temperature are developed using four statistical methods: principal component regression (PCR), composite plus scale (CPS), Bayesian hierarchical models (LNA), and pairwise comparison (PaiCo). The reconstructions are then compared with a three-member ensemble of GISS-E2-R climate model simulations and independent paleoclimate records. Decadal fluctuations in Australasian temperatures are remarkably similar between the four reconstruction methods. There are, however, differences in the amplitude of temperature variations between the different statistical methods and proxy networks. When the R28 network is used, the warmest 30-yr periods occur after 1950 in 77% of ensemble members over all methods. However, reconstructions based on only the longest records (R2 and R3 networks) indicate that single 30- and 10-yr periods of similar or slightly higher temperatures than in the late twentieth century may have occurred during the first half of the millennium. Regardless, the most recent instrumental temperatures (1985–2014) are above the 90th percentile of all 12 reconstruction ensembles (four reconstruction methods based on three proxy networks—R28, R3, and R2). The reconstructed twentieth-century warming cannot be explained by natural variability alone using GISS-E2-R. In this climate model, anthropogenic forcing is required to produce the rate and magnitude of post-1950 warming observed in the Australasian region. These paleoclimate results are consistent with other studies that attribute the post-1950 warming in Australian temperature records to increases in atmospheric greenhouse gas concentrations.

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Steven J. Phipps, Helen V. McGregor, Joëlle Gergis, Ailie J. E. Gallant, Raphael Neukom, Samantha Stevenson, Duncan Ackerley, Josephine R. Brown, Matt J. Fischer, and Tas D. van Ommen

Abstract

The past 1500 years provide a valuable opportunity to study the response of the climate system to external forcings. However, the integration of paleoclimate proxies with climate modeling is critical to improving the understanding of climate dynamics. In this paper, a climate system model and proxy records are therefore used to study the role of natural and anthropogenic forcings in driving the global climate. The inverse and forward approaches to paleoclimate data–model comparison are applied, and sources of uncertainty are identified and discussed. In the first of two case studies, the climate model simulations are compared with multiproxy temperature reconstructions. Robust solar and volcanic signals are detected in Southern Hemisphere temperatures, with a possible volcanic signal detected in the Northern Hemisphere. The anthropogenic signal dominates during the industrial period. It is also found that seasonal and geographical biases may cause multiproxy reconstructions to overestimate the magnitude of the long-term preindustrial cooling trend. In the second case study, the model simulations are compared with a coral δ 18O record from the central Pacific Ocean. It is found that greenhouse gases, solar irradiance, and volcanic eruptions all influence the mean state of the central Pacific, but there is no evidence that natural or anthropogenic forcings have any systematic impact on El Niño–Southern Oscillation. The proxy climate relationship is found to change over time, challenging the assumption of stationarity that underlies the interpretation of paleoclimate proxies. These case studies demonstrate the value of paleoclimate data–model comparison but also highlight the limitations of current techniques and demonstrate the need to develop alternative approaches.

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