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Carol F. McSweeney, Richard G. Jones, and Ben B. B. Booth

Abstract

Climate model ensembles, such as the Coupled Model Intercomparison Project, phase 3 (CMIP3), are used to characterize broadscale ranges of projected regional climate change and their impacts. The 17-member Hadley Centre perturbed physics GCM ensemble [Quantifying Uncertainty in Model Predictions (“QUMP”)] extends this capability by including data enabling dynamical downscaling of these ranges, and similar data are now being made available from the CMIP phase 5 (CMIP5) GCMs. These raise new opportunities to provide and apply high-resolution regional climate projections. This study highlights the importance of employing a well-considered sampling strategy from available ensembles to provide scientifically credible information on regional climate change while minimizing the computational complexity of ensemble downscaling.

A subset of the QUMP ensemble is selected for a downscaling program in Vietnam using the Providing Regional Climates for Impacts Studies (PRECIS) regional climate modeling system. Multiannual mean fields from each GCM are assessed with a focus on the Asian summer monsoon, given its importance to proposed applications of the projections. First, the study examines whether any model should be eliminated because significant deficiencies in its simulation may render its future climate projections unrealistic. No evidence is found to eliminate any of the 17 GCMs on these grounds. Second, the range of their future projections is explored and five models that best represent the full range of future climates are identified. The subset characterizes the range of both global and regional responses, and patterns of rainfall response, the wettest and driest projections for Vietnam, and different projected Asian summer monsoon changes. How these ranges of responses compare with those in the CMIP3 ensemble are also assessed, finding differences in both the signal and the spread of results in Southeast Asia.

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Elizabeth J. Kendon, Richard G. Jones, Erik Kjellström, and James M. Murphy

Abstract

Multimodel ensembles, whereby different global climate models (GCMs) and regional climate models (RCMs) are combined, have been widely used to explore uncertainties in regional climate projections. In this study, the extent to which information can be enhanced from sparsely filled GCM–RCM ensemble matrices and the way in which simulations should be prioritized to sample uncertainties most effectively are examined.

A simple scaling technique, whereby the local climate response in an RCM is predicted from the large-scale change in the GCM, is found to often show skill in estimating local changes for missing GCM–RCM combinations. In particular, scaling shows skill for precipitation indices (including mean, variance, and extremes) across Europe in winter and mean and extreme temperature in summer and winter, except for hot extremes over central/northern Europe in summer. However, internal variability significantly impacts the ability to determine scaling skill for precipitation indices, with a three-member ensemble found to be insufficient for identifying robust local scaling relationships in many cases.

This study suggests that, given limited computer resources, ensembles should be designed to prioritize the sampling of GCM uncertainty, using a reduced set of RCMs. Exceptions are found over the Alps and northeastern Europe in winter and central Europe in summer, where sampling multiple RCMs may be equally or more important for capturing uncertainty in local temperature or precipitation change. This reflects the significant role of local processes in these regions. Also, to determine the ensemble strategy in some cases, notably precipitation extremes in summer, better sampling of internal variability is needed.

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Elizabeth J. Kendon, David P. Rowell, Richard G. Jones, and Erasmo Buonomo

Abstract

Reliable projections of future changes in local precipitation extremes are essential for informing policy decisions regarding mitigation and adaptation to climate change. In this paper, the extent to which the natural variability of the climate affects one’s ability to project the anthropogenically forced component of change in daily precipitation extremes across Europe is examined. A three-member ensemble of the Hadley Centre Regional Climate Model (HadRM3H) is used and a statistical framework is applied to estimate the uncertainty due to the full spectrum of climate variability. In particular, the results and understanding presented here suggest that annual to multidecadal natural variability may contribute significant uncertainty. For this ensemble projection, extreme precipitation changes at the grid-box level are found to be discernible above climate noise over much of northern and central Europe in winter, and parts of northern and southern Europe in summer. The ability to quantify the change to a reasonable level of accuracy is largely limited to regions in northern Europe. In general, where climate noise has a significant component varying on decadal time scales, single 30-yr climate change projections are insufficient to infer changes in the extreme tail of the underlying precipitation distribution. In this context, the need for ensembles of integrations is demonstrated and the relative effectiveness of spatial pooling and averaging for generating robust signals of extreme precipitation change is also explored. The key conclusions are expected to apply more generally to other models and forcing scenarios.

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Philip W. Mote, Myles R. Allen, Richard G. Jones, Sihan Li, Roberto Mera, David E. Rupp, Ahmed Salahuddin, and Dean Vickers

Abstract

Computing resources donated by volunteers have generated the first superensemble of regional climate model results, in which the Hadley Centre Regional Model, version 3P (HadRM3P), and Hadley Centre Atmosphere Model, version 3P (HadAM3P), were implemented for the western United States at 25-km resolution. Over 136,000 valid and complete 1-yr runs have been generated to date: about 126,000 for 1960–2009 using observed sea surface temperatures (SSTs) and 10,000 for 2030–49 using projected SSTs from a global model simulation. Ensemble members differ in initial conditions, model physics, and (potentially, for future runs) SSTs. This unprecedented confluence of high spatial resolution and large ensemble size allows high signal-to-noise ratio and more robust estimates of uncertainty. This paper describes the experiment, compares model output with observations, shows select results for climate change simulations, and gives examples of the strength of the large ensemble size.

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Victoria A. Bell, Nicola Gedney, Alison L. Kay, Roderick N. B. Smith, Richard G. Jones, and Robert J. Moore

Abstract

River basin managers concerned with maintaining water supplies and mitigating flood risk in the face of climate change are taking outputs from climate models and using them in hydrological models for assessment purposes. While precipitation is the main output used, evaporation is attracting increasing attention because of its significance to the water balance of river basins. Climate models provide estimates of actual evaporation that are consistent with their simplified land surface schemes but do not naturally provide the estimates of potential evaporation (PE) commonly required as input to hydrological models. There are clear advantages in using PE estimates controlled by atmospheric forcings when using stand-alone hydrological models with integral soil-moisture accounting schemes. The atmosphere–land decoupling approximation that PE provides can prove to be of further benefit if it is possible to account for the effect of different, or changing, land cover on PE outside of the climate model. The methods explored here estimate Penman–Monteith PE from vegetated surfaces using outputs from climate models that have an embedded land surface scheme. The land surface scheme enables an examination of the dependence of canopy stomatal resistance on atmospheric composition, and the sensitivity of PE estimates to the choice of canopy resistance values under current and changing climates is demonstrated. The conclusions have practical value for climate change impact studies relating to flood, drought, and water management applications.

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Ranjini Swaminathan, Robert J. Parker, Colin G. Jones, Richard P. Allan, Tristan Quaife, Douglas I. Kelley, Lee de Mora, and Jeremy Walton

Abstract

A key goal of the 2015 Paris Climate Agreement is to keep global mean temperature change at 2°C and if possible under 1.5°C by the end of the century. To investigate the likelihood of achieving this target, we calculate the year of exceedance of a given global warming threshold (GWT) temperature across 32 CMIP6 models for Shared Socioeconomic Pathway (SSP) and radiative forcing combinations included in the Tier 1 ScenarioMIP simulations. Threshold exceedance year calculations reveal that a majority of CMIP6 models project warming beyond 2°C by the end of the century under every scenario or pathway apart from the lowest emission scenarios considered, SSP1–1.9 and SSP1–2.6, which is largely a function of the ScenarioMIP experiment design. The U.K. Earth System Model (UKESM1) ScenarioMIP projections are analyzed in detail to assess the regional and seasonal variations in climate at different warming levels. The warming signal emerging by midcentury is identified as significant and distinct from internal climate variability in all scenarios considered and includes warming summers in the Mediterranean, drying in the Amazon, and heavier Indian monsoons. Arctic sea ice depletion results in prominent amplification of warming and tropical warming patterns emerge that are distinct from interannual variability. Climate changes projected for a 2°C warmer world are in almost all cases exacerbated with further global warming (e.g., to a 4°C warmer world).

Open access
Friederike E.L. Otto, Luke J. Harrington, David Frame, Emily Boyd, Kristian Cedervall Lauta, Michael Wehner, Ben Clarke, Emmanuel Raju, Chad Boda, Mathias Hauser, Rachel A. James, and Richard G. Jones

Capsule:

Currently no systematic assessment of loss and damage due to climate change exists. Towards such an inventory we present a transparent way to ascertain the quality of evidence for such assessments.

Current levels of global warming (Haustein et al. 2017) have already intensified heatwaves, droughts and floods, with many recent events exhibiting evidence of being exacerbated by anthropogenic climate change (e.g., Herring et al. 2018, 2016). Recent improvements in understanding demonstrate that half a degree of additional warming will have further severe impacts (Masson-Delmotte et al. 2018). In the context of this rapid and damaging change, there is a clear need to quantify and address both the losses and damages from impacts we have not adapted to today, as well as to adapt to those that will emerge in the next few decades. To do this, it is essential to understand the impacts of man-made climate change on the scales that climate adaptation decisions are made. Drivers of disasters, ultimately responsible for much loss and damage, are unfolding in an ever-changing socio-economic context, which also alters exposure and vulnerability. While various case studies exist (discussed below), there is to date no comprehensive or comparable database quantifying anthropogenic contributions to climate change loss and damage. We suggest that this needs to change.

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Friederike E. L. Otto, Luke J. Harrington, David Frame, Emily Boyd, Kristian Cedervall Lauta, Michael Wehner, Ben Clarke, Emmanuel Raju, Chad Boda, Mathias Hauser, Rachel A. James, and Richard G. Jones

Current levels of global warming (Haustein et al. 2017) have already intensified heat waves, droughts, and floods, with many recent events exhibiting evidence of being exacerbated by anthropogenic climate change (e.g., Herring et al. 2016, 2018). Recent improvements in understanding demonstrate that half a degree of additional warming will have further severe impacts (Masson-Delmotte et al. 2018). In the context of this rapid and damaging change, there is a clear need to quantify and address both the losses and damages from impacts we have not adapted to today, as well as to

Free access
Catherine A. Senior, John H. Marsham, Ségolène Berthou, Laura E. Burgin, Sonja S. Folwell, Elizabeth J. Kendon, Cornelia M. Klein, Richard G. Jones, Neha Mittal, David P. Rowell, Lorenzo Tomassini, Théo Vischel, Bernd Becker, Cathryn E. Birch, Julia Crook, Andrew J. Dougill, Declan L. Finney, Richard J. Graham, Neil C. G. Hart, Christopher D. Jack, Lawrence S. Jackson, Rachel James, Bettina Koelle, Herbert Misiani, Brenda Mwalukanga, Douglas J. Parker, Rachel A. Stratton, Christopher M. Taylor, Simon O. Tucker, Caroline M. Wainwright, Richard Washington, and Martin R. Willet

Abstract

Pan-Africa convection-permitting regional climate model simulations have been performed to study the impact of high resolution and the explicit representation of atmospheric moist convection on the present and future climate of Africa. These unique simulations have allowed European and African climate scientists to understand the critical role that the representation of convection plays in the ability of a contemporary climate model to capture climate and climate change, including many impact-relevant aspects such as rainfall variability and extremes. There are significant improvements in not only the small-scale characteristics of rainfall such as its intensity and diurnal cycle, but also in the large-scale circulation. Similarly, effects of explicit convection affect not only projected changes in rainfall extremes, dry spells, and high winds, but also continental-scale circulation and regional rainfall accumulations. The physics underlying such differences are in many cases expected to be relevant to all models that use parameterized convection. In some cases physical understanding of small-scale change means that we can provide regional decision-makers with new scales of information across a range of sectors. We demonstrate the potential value of these simulations both as scientific tools to increase climate process understanding and, when used with other models, for direct user applications. We describe how these ground-breaking simulations have been achieved under the U.K. Government’s Future Climate for Africa Programme. We anticipate a growing number of such simulations, which we advocate should become a routine component of climate projection, and encourage international coordination of such computationally and human-resource expensive simulations as effectively as possible.

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Catherine A Senior, John H Marsham, Sègoléne Berthou, Laura E Burgin, Sonja S Folwell, Elizabeth J Kendon, Cornelia M Klein, Richard G Jones, Neha Mittal, David P Rowell, Lorenzo Tomassini, Thèo Vischel, Bernd Becker, Cathryn E Birch, Julia Crook, Andrew J Dougill, Declan L Finney, Richard J Graham, Neil C G Hart, Christopher D Jack, Lawrence S Jackson, Rachel James, Bettina Koelle, Herbert Misiani, Brenda Mwalukanga, Douglas J Parker, Rachel A Stratton, Christopher M Taylor, Simon O Tucker, Caroline M Wainwright, Richard Washington, and Martin R Willet

Abstract

Pan-Africa convection-permitting regional climate model simulations have been performed to study the impact of high resolution and the explicit representation of atmospheric moist convection on the present and future climate of Africa. These unique simulations have allowed European and African climate scientists to understand the critical role that the representation of convection plays in the ability of a contemporary climate model to capture climate and climate change, including many impact relevant aspects such as rainfall variability and extremes. There are significant improvements in not only the small-scale characteristics of rainfall such as its intensity and diurnal cycle, but also in the large-scale circulation. Similarly effects of explicit convection affect not only projected changes in rainfall extremes, dry-spells and high winds, but also continental-scale circulation and regional rainfall accumulations. The physics underlying such differences are in many cases expected to be relevant to all models that use parameterized convection. In some cases physical understanding of small-scale change mean that we can provide regional decision makers with new scales of information across a range of sectors. We demonstrate the potential value of these simulations both as scientific tools to increase climate process understanding and, when used with other models, for direct user applications. We describe how these ground-breaking simulations have been achieved under the UK Government’s Future Climate for Africa Programme. We anticipate a growing number of such simulations, which we advocate should become a routine component of climate projection, and encourage international co-ordination of such computationally, and human-resource expensive simulations as effectively as possible.

Full access