Recent coordinated efforts, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multimodel ensembles sample initial conditions, parameters, and structural uncertainties in the model design, and they have prompted a variety of approaches to quantifying uncertainty in future climate change. International climate change assessments also rely heavily on these models. These assessments often provide equal-weighted averages as best-guess results, assuming that individual model biases will at least partly cancel and that a model average prediction is more likely to be correct than a prediction from a single model based on the result that a multimodel average of present-day climate generally outperforms any individual model. This study outlines the motivation for using multimodel ensembles and discusses various challenges in interpreting them. Among these challenges are that the number of models in these ensembles is usually small, their distribution in the model or parameter space is unclear, and that extreme behavior is often not sampled. Model skill in simulating present-day climate conditions is shown to relate only weakly to the magnitude of predicted change. It is thus unclear by how much the confidence in future projections should increase based on improvements in simulating present-day conditions, a reduction of intermodel spread, or a larger number of models. Averaging model output may further lead to a loss of signal— for example, for precipitation change where the predicted changes are spatially heterogeneous, such that the true expected change is very likely to be larger than suggested by a model average. Last, there is little agreement on metrics to separate ‘‘good’’ and ‘‘bad’’ models, and there is concern that model development, evaluation, and posterior weighting or ranking are all using the same datasets. While the multimodel average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.

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Challenges in Combining Projections from Multiple Climate Models

Despite their adverse impacts, definitions and measurements of heat waves are ambiguous and inconsistent, generally being endemic to only the group affected, or the respective study reporting the analysis. The present study addresses this issue by employing a set of three heat wave definitions, derived from surveying heat-related indices in the climate science literature. The definitions include three or more consecutive days above one of the following: the 90th percentile for maximum temperature, the 90th percentile for minimum temperature, and positive extreme heat factor (EHF) conditions. Additionally, each index is studied using a multiaspect framework measuring heat wave number, duration, participating days, and the peak and mean magnitudes. Observed climatologies and trends computed by Sen's Kendall slope estimator are presented for the Australian continent for two time periods (1951–2008 and 1971–2008). Trends in all aspects and definitions are smaller in magnitude but more significant for ...

On the Measurement of Heat Waves

Extreme weather and climate-related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human-induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers. WIREs Clim Change 2016, 7:23-41. doi: 10.1002/wcc.380 For further resources related to this article, please visit the WIREs website.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/wcc.380

Attribution of extreme weather and climate-related events

The authors assess the ability of 18 Earth system models to simulate the land and ocean carbon cycle for the present climate. These models will be used in the next Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) for climate projections, and such evaluation allows identification of the strengths and weaknesses of individual coupled carbon–climate models as well as identification of systematic biases of the models. Results show that models correctly reproduce the main climatic variables controlling the spatial and temporal characteristics of the carbon cycle. The seasonal evolution of the variables under examination is well captured. However, weaknesses appear when reproducing specific fields: in particular, considering the land carbon cycle, a general overestimation of photosynthesis and leaf area index is found for most of the models, while the ocean evaluation shows that quite a few models underestimate the primary production.The authors also propose climate and car...

https://journals.ametsoc.org/doi/pdf/10.1175/JCLI-D-12-00417.1

Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models

Multiple simulations from nine globally coupled climate models were assessed for their ability to reproduce observed trends in a set of indices representing temperature and precipitation extremes over Australia. Observed trends over the period 1957–1999 were compared with individual and multi-modelled trends calculated over the same period. When averaged across Australia, the magnitude of trends and interannual variability of temperature extremes were well simulated by most models, particularly for the index for warm nights. The majority of models also reproduced the correct sign of trend for precipitation extremes although there was much more variation between the individual model runs. A bootstrapping technique was used to calculate uncertainty estimates and also to verify that most model runs produce plausible trends when averaged over Australia. Although very few showed significant skill at reproducing the observed spatial pattern of trends, a pattern correlation measure showed that spatial noise could not be ruled out as dominating these patterns. Two of the models with output from different forcings showed that the observed trends over Australia for one of the temperature indices was consistent with an anthropogenic response, but was inconsistent with natural-only forcings. Future projected changes in extremes using three emissions scenarios were also analysed. Australia shows a shift towards warming of temperature extremes, particularly a significant increase in the number of warm nights and heat waves with much longer dry spells interspersed with periods of increased extreme precipitation, irrespective of the scenario used. Copyright © 2008 Royal Meteorological Society

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Assessing trends in observed and modelled climate extremes over Australia in relation to future projections

The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced Across all three variables, the coupled climate models perform better than expected Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models Averaged over Australia, 3 of the 14 climate models capture more than 80% of the observed probability density functions for precipitation Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80% of the observed probability densit

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Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions

SE Perkins

Papers

Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions