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Sophie C. Lewis
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Sophie C. Lewis

Abstract

Knowledge of the range of precipitation variability and extremes is restricted in regions such as Australia, where instrumental records are short and paleoclimatic records are limited in spatial and temporal extent and resolution. In such comparatively data-poor regions, there is limited context for understanding the statistical unusualness of recently observed extreme events, such as heavy rain and drought, and the influence of stochastic and anthropogenic forcings on their magnitude. This study attempts to further understandings of the range of forced and unforced variability using CMIP5 climate models. Focusing on extremes in the magnitude of monthly, seasonal, and annual precipitation, the distribution of instrumental-period observed precipitation in various Australian regions is compared to simulated precipitation in historical experiments as well as various long experiment (preindustrial control and Last Millennium) and anthropogenically forced simulations of the twenty-first century (RCP2.6 and RCP8.5). There is no systematic increase in the magnitude of simulated extremes corresponding to the length of model simulations, although many realizations reveal higher magnitude extremes compared to those observed, suggesting that the duration of the instrumental record may not capture the potential severity of stochastically driven extremes. A coherent increase in both wet and dry extremes is simulated throughout Australian regions in high greenhouse gas emissions scenarios, demonstrating a forced hydrological response.

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Sophie C. Lewis and Jennie Mallela
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Sophie C. Lewis and David J. Karoly

Abstract

Diurnal temperature range (DTR) is a useful index of climatic change in addition to mean temperature changes. Observational records indicate that DTR has decreased over the last 50 yr because of differential changes in minimum and maximum temperatures. However, modeled changes in DTR in previous climate model simulations of this period are smaller than those observed, primarily because of an overestimate of changes in maximum temperatures. This present study examines DTR trends using the latest generation of global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and utilizes the novel CMIP5 detection and attribution experimental design of variously forced historical simulations (natural-only, greenhouse gas–only, and all anthropogenic and natural forcings). Comparison of observed and modeled changes in DTR over the period of 1951–2005 again reveals that global DTR trends are lower in model simulations than observed across the 27-member multimodel ensemble analyzed here. Modeled DTR trends are similar for both experiments incorporating all forcings and for the historical experiment with greenhouse gases only, while no DTR trend is discernible in the naturally forced historical experiment. The persistent underestimate of DTR changes in this latest multimodel evaluation appears to be related to ubiquitous model deficiencies in cloud cover and land surface processes that impact the accurate simulation of regional minimum or maximum temperatures changes observed during this period. Different model processes are likely responsible for subdued simulated DTR trends over the various analyzed regions.

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Sophie C. Lewis, Andrew D. King, and Sarah E. Perkins-Kirkpatrick

Abstract

The term “new normal” has been used in scientific literature and public commentary to contextualize contemporary climate events as an indicator of a changing climate due to enhanced greenhouse warming. A new normal has been used broadly but tends to be descriptive and ambiguously defined. Here we review previous studies conceptualizing this idea of a new climatological normal and argue that this term should be used cautiously and with explicit definition in order to avoid confusion. We provide a formal definition of a new climate normal relative to present based around record-breaking contemporary events and explore the timing of when such extremes become statistically normal in the future model simulations. Applying this method to the record-breaking global-average 2015 temperatures as a reference event and a suite of model climate models, we determine that 2015 global annual-average temperatures will be the new normal by 2040 in all emissions scenarios. At the regional level, a new normal can be delayed through aggressive greenhouse gas emissions reductions. Using this specific case study to investigate a climatological new normal, our approach demonstrates the greater value of the concept of a climatological new normal for understanding and communicating climate change when the term is explicitly defined. This approach moves us one step closer to understanding how current extremes will change in the future in a warming world.

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Andrea J. Dittus, David J. Karoly, Sophie C. Lewis, Lisa V. Alexander, and Markus G. Donat

Abstract

The skill of eight climate models in simulating the variability and trends in the observed areal extent of daily temperature and precipitation extremes is evaluated across five large-scale regions, using the climate extremes index (CEI) framework. Focusing on Europe, North America, Asia, Australia, and the Northern Hemisphere, results show that overall the models are generally able to simulate the decadal variability and trends of the observed temperature and precipitation components over the period 1951–2005. Climate models are able to reproduce observed increasing trends in the area experiencing warm maximum and minimum temperature extremes, as well as, to a lesser extent, increasing trends in the areas experiencing an extreme contribution of heavy precipitation to total annual precipitation for the Northern Hemisphere regions. Using simulations performed under different radiative forcing scenarios, the causes of simulated and observed trends are investigated. A clear anthropogenic signal is found in the trends in the maximum and minimum temperature components for all regions. In North America, a strong anthropogenically forced trend in the maximum temperature component is simulated despite no significant trend in the gridded observations, although a trend is detected in a reanalysis product. A distinct anthropogenic influence is also found for trends in the area affected by a much-above-average contribution of heavy precipitation to annual precipitation totals for Europe in a majority of models and to varying degrees in other Northern Hemisphere regions. However, observed trends in the area experiencing extreme total annual precipitation and extreme number of wet and dry days are not reproduced by climate models under any forcing scenario.

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Andrea J. Dittus, David J. Karoly, Sophie C. Lewis, and Lisa V. Alexander

Abstract

This study examines trends in the area affected by temperature and precipitation extremes across five large-scale regions using the climate extremes index (CEI) framework. Analyzing changes in temperature and precipitation extremes in terms of areal fraction provides information from a different perspective and can be useful for climate monitoring. Trends in five temperature and precipitation components are analyzed, calculated using a new method based on standard extreme indices. These indices, derived from daily meteorological station data, are obtained from two global land-based gridded extreme indices datasets. The four continental-scale regions of Europe, North America, Asia, and Australia are analyzed over the period from 1951 to 2010, where sufficient data coverage is available. These components are also computed for the entire Northern Hemisphere, providing the first CEI results at the hemispheric scale. Results show statistically significant increases in the percentage area experiencing much-above-average warm days and nights and much-below-average cool days and nights for all regions, with the exception of North America for maximum temperature extremes. Increases in the area affected by precipitation extremes are also found for the Northern Hemisphere regions, particularly Europe and North America.

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Andrew D. King, Reto Knutti, Peter Uhe, Daniel M. Mitchell, Sophie C. Lewis, Julie M. Arblaster, and Nicolas Freychet

Abstract

Given the Paris Agreement it is imperative there is greater understanding of the consequences of limiting global warming to the target 1.5° and 2°C levels above preindustrial conditions. It is challenging to quantify changes across a small increment of global warming, so a pattern-scaling approach may be considered. Here we investigate the validity of such an approach by comprehensively examining how well local temperatures and warming trends in a 1.5°C world predict local temperatures at global warming of 2°C. Ensembles of transient coupled climate simulations from multiple models under different scenarios were compared and individual model responses were analyzed. For many places, the multimodel forced response of seasonal-average temperatures is approximately linear with global warming between 1.5° and 2°C. However, individual model results vary and large contributions from nonlinear changes in unforced variability or the forced response cannot be ruled out. In some regions, such as East Asia, models simulate substantially greater warming than is expected from linear scaling. Examining East Asia during boreal summer, we find that increased warming in the simulated 2°C world relative to scaling up from 1.5°C is related to reduced anthropogenic aerosol emissions. Our findings suggest that, where forcings other than those due to greenhouse gas emissions change, the warming experienced in a 1.5°C world is a poor predictor for local climate at 2°C of global warming. In addition to the analysis of the linearity in the forced climate change signal, we find that natural variability remains a substantial contribution to uncertainty at these low-warming targets.

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Sophie C. Lewis, Stephanie A.P. Blake, Blair Trewin, Mitchell T. Black, Andrew J. Dowdy, Sarah E. Perkins-Kirkpatrick, Andrew D. King, and Jason J. Sharples
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