Neil Massey

Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models

In complex spatial models, as used to predict the climate response to greenhouse gas emissions, parameter variation within plausible bounds has major effects on model behavior of interest. Here, we present an unprecedentedly large ensemble of >57,000 climate model runs in which 10 parameters, initial conditions, hardware, and software used to run the model all have been varied. We relate information about the model runs to large-scale model behavior (equilibrium sensitivity of global mean temperature to a doubling of carbon dioxide). We demonstrate that effects of parameter, hardware, and software variation are detectable, complex, and interacting. However, we find most of the effects of parameter variation are caused by a small subset of parameters. Notably, the entrainment coefficient in clouds is associated with 30% of the variation seen in climate sensitivity, although both low and high values can give high climate sensitivity. We demonstrate that the effect of hardware and software is small relative to the effect of parameter variation and, over the wide range of systems tested, may be treated as equivalent to that caused by changes in initial conditions. We discuss the significance of these results in relation to the design and interpretation of climate modeling experiments and large-scale modeling more generally.

Anthropogenic influence on the changing likelihood of an exceptionally warm summer in Texas, 2011

The impact of anthropogenic forcing on the probability of high mean summer temperatures being exceeded in Texas in the year 2011 was investigated using an atmospheric circulation model to simulate large ensembles of the world with 2011 level forcing and 5 “counterfactual” worlds under preindustrial forcing. In Texas, drought is a strong control on summer temperature, so an increased frequency in large precipitation deficits and/or soil moisture deficits that may result from anthropogenic forcing could magnify the regional footprint of global warming. However, no simulated increase in the frequency of large precipitation deficits, or of soil moisture deficits, was detected from preindustrial to year 2011 conditions. Despite the lack of enhancement to warming via these potential changes in the hydrological cycle, the likelihood of a given unusually high summer temperature being exceeded was simulated to be about 10 times greater due to anthropogenic emissions.

Attribution analysis of high precipitation events in summer in England and Wales over the last decade

The crucial question in the public debate of extreme events is increasingly whether and to what extent the event has been caused by anthropogenic warming. In this study we investigate this question using extreme summer precipitation events in England and Wales as an example for probabilistic event attribution using very large ensembles of regional climate model (RCM) simulations within the weather@home.net project. This allows us to analyse the statistics of high precipitation events in England and Wales, a region with a high quality precipitation observational dataset. Validating the model simulations against observations shows a credible shape of the distribution of 5-day precipitation, and thus confidence in the results. While the risk of extreme July precipitation events has at least doubled due to anthropogenic climate change in the modelling framework, no significant changes can be detected for the other two summer months. This study thus highlights the challenges of probabilistic event attribution of complex weather events and identifies the need to further decompose atmospheric features responsible for an event to occur for quantitative attribution analysis.

Feature tracking on the hierarchical equal area triangular mesh

A novel scheme is presented to enable the identification of weather-like features in meteorological data along with a method to track the features across timesteps. The scheme defines a hierarchical triangular mesh produced by repeatedly subdividing an icosahedron. The surface area of each triangle in the mesh is equalised by an iterative application of vertex repulsion, analogous to Coulombs law. The scheme regrids meteorological data to the triangular mesh via a point inclusion test and area-weighted averaging. Weather-like feature points are detected in the regridded data via a search for extrema in adjacent triangles. These feature points are transformed into objects, such as a low pressure system, by growing the feature point into an area where the sign of the second derivative of the data remains unchanged. The centre points of these objects are tracked across timesteps by the application of a cost minimising algorithm which guarantees locally optimal tracks. By using this suite of methods, weather-like features are identified consistently at high and low latitudes, all weather-like features remain on the same scale and the tracking of features at high latitudes and across the Poles is enabled. These properties are demonstrated by test cases using synthetic data.

Ensemble of European regional climate simulations for the winter of 2013 and 2014 from HadAM3P-RM3P

Large data sets used to study the impact of anthropogenic climate change on the 2013/14 floods in the UK are provided. The data consist of perturbed initial conditions simulations using the Weather@Home regional climate modelling framework. Two different base conditions, Actual, including atmospheric conditions (anthropogenic greenhouse gases and human induced aerosols) as at present and Natural, with these forcings all removed are available. The data set is made up of 13 different ensembles (2 actual and 11 natural) with each having more than 7500 members. The data is available as NetCDF V3 files representing monthly data within the period of interest (1st Dec 2013 to 15th February 2014) for both a specified European region at a 50 km horizontal resolution and globally at N96 resolution. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository.

Influence of the Ocean and Greenhouse Gases on Severe Drought Likelihood in the Central United States in 2012

AbstractThe impacts of sea surface temperature (SST) anomalies and anthropogenic greenhouse gases on the likelihood of extreme drought occurring in the central United States in the year 2012 were investigated using large-ensemble simulations from a global atmospheric climate model. Two sets of experiments were conducted. In the first, the simulated hydroclimate of 2012 was compared to a baseline period (1986–2014) to investigate the impact of SSTs. In the second, the hydroclimate in a world with 2012-level anthropogenic forcing was compared to five “counterfactual” versions of a 2012 world under preindustrial forcing. SST anomalies in 2012 increased the simulated likelihood of an extreme summer precipitation deficit (e.g., the deficit with a 2% exceedance probability) by a factor of 5. The likelihood of an extreme summer soil moisture deficit increased by a similar amount, due in great part to a large spring soil moisture deficit carrying over into summer. An anthropogenic impact on precipitation was dete...

Feature tracking in high-resolution regional climate data

In this paper, a suite of algorithms are presented which facilitate the identification and tracking of storm-indicative features, such as mean sea-level pressure minima, in high resolution regional climate data. The methods employ a hierarchical triangular mesh, which is tailored to the regional climate data by only subdividing triangles, from an initial icosahedron, within the domain of the data. The regional data is then regridded to this triangular mesh at each level of the grid, producing a compact representation of the data at numerous resolutions. Storm indicative features are detected by first subtracting the background field, represented by a low resolution version of the data, which occurs at a lower level in the mesh. Anomalies from this background field are detected, as feature objects, at a mesh level which corresponds to the spatial scale of the feature being detected and then refined to the highest mesh level. These feature objects are expanded to an outer contour and overlapping objects are merged. The centre points of these objects are tracked across timesteps by applying an optimisation scheme which uses five hierarchical rules. Objects are added to tracks based on the highest rule in the scheme they pass and, if two objects pass the same rule, the cost of adding the object to the track. An object exchange scheme ensures that adding an object to a track is locally optimal. An additional track optimisation phase is performed which exchanges segments between tracks and merges tracks to obtain a globally optimal track set. To validate the suite of algorithms they are applied to the ERA-Interim reanalysis dataset and compared to other storm-indicative feature tracking algorithms. HighlightsA hierarchical triangular mesh is created for a region by dividing an icosahedron.Regional data is regridded to the mesh, producing a compact representation.Detection of stormHYPHENindicative features by search of anomalies from background field.Locally optimal tracking of features by passing a set of hierarchical rules.Global optimisation of the track set by merging and exchanging track sections.