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James Murphy

Abstract

An assessment is made of downscaling estimates of screen temperature and precipitation observed at 976 European stations during 1983–94. A statistical downscaling technique, in which local values are inferred from observed atmospheric predictor variables, is compared against two dynamical downscaling techniques, based on the use of the screen temperature or precipitation simulated at the nearest grid point in integrations of two climate models. In one integration a global general circulation model (GCM) is constrained to reproduce the observed atmospheric circulation over the period of interest, while the second involves a high-resolution regional climate model (RCM) nested inside the GCM.

The dynamical and statistical methods are compared in terms of the correlation between the estimated and observed time series of monthly anomalies. For estimates of temperature a high degree of skill is found, especially over western, central, and northern Europe; for precipitation skill is lower (average correlations ranging from 0.4 in summer to 0.7 in winter). Overall, the dynamical and statistical methods show similar levels of skill, although the statistical method is better for summertime estimates of temperature while the dynamical methods give slightly better estimates of wintertime precipitation. In general, therefore, the skill with which present-day surface climate anomalies can be derived from atmospheric observations is not improved by using the sophisticated calculations of subgrid-scale processes made in climate models rather than simple empirical relationships. It does not necessarily follow that statistical and dynamical downscaling estimates of changes in surface climate will also possess equal skill.

By the above measure the two dynamical techniques possess approximately equal skill; however, they are also compared by assessing errors in the mean and variance of monthly values and errors in the simulated distributions of daily values. Such errors arise from systematic biases in the models plus the effect of unresolved local forcings. For precipitation the results show that the RCM offers clear benefits relative to the GCM: the simulated variability of both daily and monthly values, although lower than observed, is much more realistic than in the GCM because the finer grid reduces the amount of spatial smoothing implicit in the use of grid-box variables. The climatological means are also simulated better in the winter half of the year because the RCM captures some of the mesoscale detail present in observed distributions. The temperature fields contain a mesoscale orographic signal that is skillfully reproduced by the RCM; however, this is not a source of increased skill relative to the GCM since elevation biases can be largely removed using simple empirical corrections based on spatially averaged lapse rates. Nevertheless, the average skill of downscaled climatological mean temperature values is higher in the RCM in nearly all months. The additional skill arises from better resolution of local physiographical features, especially coastlines, and also from the dynamical effects of higher resolution, which generally act to reduce the large-scale systematic biases in the simulated values. Both models tend to overestimate the variability of both daily and monthly mean temperature. On average the RCM is more skillful in winter but less skillful in summer, due to excessive drying of the soil over central and southern Europe.

The downscaling scores for monthly means are compared against scores obtained by using a predictor variable consisting of observations from the nearest station to the predictand station. In general the downscaling scores are significantly worse than those obtained from adjacent stations, indicating that there remains considerable scope for refining the techniques in future. In the case of dynamical downscaling progress can be made by reducing systematic errors through improvements in the representation of physical processes and increased resolution; the prospects for improving statistical downscaling will depend on the availability of the observational data needed to provide longer calibration time series and/or a wider range of predictor variables.

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James O’Donnell, Arthur A. Allen, and Donald L. Murphy

Abstract

Drogued floats have been widely used to develop information about the Lagrangian structure of ocean circulation. However, the degree to which the various designs actually follow water parcels has been the subject of considerable debate in the literature. The design developed for the First GARP (Global Atmospheric Research Program) Global Experiment (FGGE) program (basically, a spar buoy and window-shade drogue with ARGOS navigation) has been in routine use for a decade in the Labrador Current, and a large dataset has been accumulated. The methods and results of a set of experiments conducted to directly measure the relative velocity of the drogue through the water are summarized.

Current components relative to the drifter were measured by two electromagnetic current meters, and the local weather and sea state were observed by a drifting meteorological buoy. Two uninstrumented drifters and an undrogued but weighted float were deployed in conjunction with the main drifter to provide an independent estimate of the drifter performance. Three deployments were successfully conducted during periods in which the wind speed varied from 0 to 10 m s−1 with wave heights up to 3.5 m.

The authors found no significant differences in the Lagrangian velocities of the FGGE drifters due to the presence of the current meters but significantly greater slip of the meteorological buoy and an undrogued drifter. These observations are consistent with a simple model of drifter performance. The current meter measurements showed relative velocities of up to 0.1 m s−1 with vertical shear across the drogue of order 10−2 s−1 with considerable high-frequency variability. Regression analysis of the data averaged in 20-h bins to remove serial correlation showed significant linear relationships between wind speed and slip in all experiments and a significant dependence on the shear in one. When all the data was combined, a linear model of the slip based on wind alone explained 60% of the observed variance and a best estimate of the slip at 10 m s−1 wind speed of 0.06 m s−1.

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Robin T. Clark, Simon J. Brown, and James M. Murphy

Abstract

Changes in extreme daily temperature events are examined using a perturbed physics ensemble of global model simulations under present-day and doubled CO2 climates where ensemble members differ in their representation of various physical processes. Modeling uncertainties are quantified by varying poorly constrained model parameters that control atmospheric processes and feedbacks and analyzing the ensemble spread of simulated changes. In general, uncertainty is up to 50% of projected changes in extreme heat events of the type that occur only once per year.

Large changes are seen in distributions of daily maximum temperatures for June, July, and August with significant shifts to warmer conditions. Changes in extremely hot days are shown to be significantly larger than changes in mean values in some regions. The intensity, duration, and frequency of summer heat waves are expected to be substantially greater over all continents. The largest changes are found over Europe, North and South America, and East Asia. Reductions in soil moisture, number of wet days, and nocturnal cooling are identified as significant factors responsible for the changes.

Although uncertainty associated with the magnitude of expected changes is large in places, it does not bring into question the sign or nature of the projected changes. Even with the most conservative simulations, hot extreme events are still expected to substantially increase in intensity, duration, and frequency. This ensemble, however, does not represent the full range of uncertainty associated with future projections; for example, the effects of multiple parameter perturbations are neglected, as are the effects of structural changes to the basic nature of the parameterization schemes in the model.

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Lisa Goddard, James W. Hurrell, Benjamin P. Kirtman, James Murphy, Timothy Stockdale, and Carolina Vera
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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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Jonathan Rougier, David M. H. Sexton, James M. Murphy, and David Stainforth

Abstract

Global climate models (GCMs) contain imprecisely defined parameters that account, approximately, for subgrid-scale physical processes. The response of a GCM to perturbations in its parameters, which is crucial for quantifying uncertainties in simulations of climate change, can—in principle—be assessed by simulating the GCM many times. In practice, however, such “perturbed physics” ensembles are small because GCMs are so expensive to simulate. Statistical tools can help in two ways. First, they can be used to combine ensembles from different but related experiments, increasing the effective number of simulations. Second, they can be used to describe the GCM’s response in ways that cannot be extracted directly from the ensemble(s). The authors combine two experiments to learn about the response of the Hadley Centre Slab Climate Model version 3 (HadSM3) climate sensitivity to 31 model parameters. A Bayesian statistical framework is used in which expert judgments are required to quantify the relationship between the two experiments; these judgments are validated by detailed diagnostics. The authors identify the entrainment rate coefficient of the convection scheme as the most important single parameter and find that this interacts strongly with three of the large-scale-cloud parameters.

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Ben B. B. Booth, Glen R. Harris, James M. Murphy, Jo I. House, Chris D. Jones, David Sexton, and Stephen Sitch

Abstract

Uncertainty in the behavior of the carbon cycle is important in driving the range in future projected climate change. Previous comparisons of model responses with historical CO2 observations have suggested a strong constraint on simulated projections that could narrow the range considered plausible. This study uses a new 57-member perturbed parameter ensemble of variants of an Earth system model for three future scenarios, which 1) explores a wider range of potential climate responses than before and 2) includes the impact of past uncertainty in carbon emissions on simulated trends. These two factors represent a more complete exploration of uncertainty, although they lead to a weaker constraint on the range of future CO2 concentrations as compared to earlier studies. Nevertheless, CO2 observations are shown to be effective at narrowing the distribution, excluding 30 of 57 simulations as inconsistent with historical CO2 changes. The perturbed model variants excluded are mainly at the high end of the future projected CO2 changes, with only 8 of the 26 variants projecting RCP8.5 2100 concentrations in excess of 1100 ppm retained. Interestingly, a minority of the high-end variants were able to capture historical CO2 trends, with the large-magnitude response emerging later in the century (owing to high climate sensitivities, strong carbon feedbacks, or both). Comparison with observed CO2 is effective at narrowing both the range and distribution of projections out to the mid-twenty-first century for all scenarios and to 2100 for a scenario with low emissions.

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Doug M. Smith, Nick J. Dunstone, Rosie Eade, David Fereday, Leon Hermanson, James M. Murphy, Holger Pohlmann, Niall Robinson, and Adam A. Scaife
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Elizabeth J. Kendon, Nigel M. Roberts, Giorgia Fosser, Gill M. Martin, Adrian P. Lock, James M. Murphy, Catherine A. Senior, and Simon O. Tucker

Abstract

For the first time, a model at a resolution on par with operational weather forecast models has been used for national climate scenarios. An ensemble of 12 climate change projections at convection-permitting (2.2 km) scale has been run for the United Kingdom, as part of the UK Climate Projections (UKCP) project. Contrary to previous studies, these show greater future increases in winter mean precipitation in the convection-permitting model compared with the coarser (12 km) driving model. A large part (60%) of the future increase in winter precipitation occurrence over land comes from an increase in convective showers in the 2.2 km model, which are most likely triggered over the sea and advected inland with potentially further development. In the 12 km model, increases in precipitation occurrence over the sea, largely due to an increase in convective showers, do not extend over the land. This is partly due to known limitations of the convection parameterization scheme, used in conventional coarse-resolution climate models, which acts locally without direct memory and so has no ability to advect diagnosed convection over the land or trigger new showers along convective outflow boundaries. This study shows that the importance of accurately representing convection extends beyond short-duration precipitation extremes and the summer season to projecting future changes in mean precipitation in winter.

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Kerri A. Pratt, Andrew J. Heymsfield, Cynthia H. Twohy, Shane M. Murphy, Paul J. DeMott, James G. Hudson, R. Subramanian, Zhien Wang, John H. Seinfeld, and Kimberly A. Prather

Abstract

During the Ice in Clouds Experiment–Layer Clouds (ICE-L), aged biomass-burning particles were identified within two orographic wave cloud regions over Wyoming using single-particle mass spectrometry and electron microscopy. Using a suite of instrumentation, particle chemistry was characterized in tandem with cloud microphysics. The aged biomass-burning particles comprised ∼30%–40% by number of the 0.1–1.0-μm clear-air particles and were composed of potassium, organic carbon, elemental carbon, and sulfate. Aerosol mass spectrometry measurements suggested these cloud-processed particles were predominantly sulfate by mass. The first cloud region sampled was characterized by primarily homogeneously nucleated ice particles formed at temperatures near −40°C. The second cloud period was characterized by high cloud droplet concentrations (∼150–300 cm−3) and lower heterogeneously nucleated ice concentrations (7–18 L−1) at cloud temperatures of −24° to −25°C. As expected for the observed particle chemistry and dynamics of the observed wave clouds, few significant differences were observed between the clear-air particles and cloud residues. However, suggestive of a possible heterogeneous nucleation mechanism within the first cloud region, ice residues showed enrichments in the number fractions of soot and mass fractions of black carbon, measured by a single-particle mass spectrometer and a single-particle soot photometer, respectively. In addition, enrichment of biomass-burning particles internally mixed with oxalic acid in both the homogeneously nucleated ice and cloud droplets compared to clear air suggests either preferential activation as cloud condensation nuclei or aqueous phase cloud processing.

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