Development, testing and example applications of the pattern-scaling approach for generating future climate change projections are reported here, with a focus on a particular software application called BClimGen^. A number of innovations have been implemented, including using exponential and logistic functions of global-mean temperature to represent changes in local precipitation and cloud cover, and interpolation from climate model grids to a finer grid while taking into account land-sea contrasts in the climate change patterns. Of particular significance is a new approach for incorporating changes in the inter-annual variability of monthly precipitation simulated by climate models. This is achieved by diagnosing simulated changes in the shape of the gamma distribution of monthly precipitation totals, applying the pattern-scaling approach to estimate changes in the shape parameter under a future scenario, and then perturbing sequences of observed precipitation anomalies so that their distribution changes according to the projected change in the shape parameter. The approach cannot represent changes to the structure of climate timeseries (e.g. changed autocorrelation or teleconnection patterns) were they to occur, but is shown here to be more successful at representing changes in low precipitation extremes than previous pattern-scaling methods.

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This study investigates changes to the annual temperature cycle in both observed records and output from a coupled ocean-atmosphere global climate model. Using least-squares harmonic analysis, changes to the observed annual harmonic (for the time period 1856-1998), in addition to the climatology, are compared with 9 simulations from the HadCM2 model. The first simulation is a 1400 yr control integration, whilst the remainder are from 2 ensembles representing (1) increases in CO 2 concentrations and (2) a combination of CO 2 and sulphate aerosol increases. Observed and simulated climatologies are generally comparable, although large amplitude and phase discrepancies exist over northern North America and high-latitude oceans, respectively. The agreement may be partly artificial over the oceans due to the use of flux adjustments to maintain a realistic sea-surface temperature field. Observed northern hemisphere amplitude decreases during the 20th century agree well with simulated changes, although there are some regional differences; observed changes to the southern hemisphere amplitude are insignificant. The sign of northern hemisphere phase changes are opposite in the 2 data sets. The nature of these results is unchanged after consideration is given to the varying spatial coverage of the observed data set, by means of applying a frozen grid mask to both observed and simulated data. These findings are consistent with previous studies, though we extend them by updating the observed record, by using ensembles to better define the climate change signal, and by considering the direct effects of sulphate aerosols. For a given warming, the inclusion of aerosols results in an enhanced amplitude decrease within the northern hemisphere, related to the summertime maximum of the direct sulphate cooling effect.KEY WORDS: Annual temperature cycle · Greenhouse gas · Sulphate aerosols · Climate change · Phase · AmplitudeResale or republication not permitted without written consent of the publisher

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Pattern scaling is widely used to create climate change projections to investigate future impacts. We consider the performance of pattern scaling for emulating the HadGEM2-ES general circulation model (GCM) paying particular attention to “high end” warming scenarios and to different choices of GCM simulations used to diagnose the climate change patterns. We demonstrate that evaluating pattern-scaling projections by comparing them with GCM simulations containing unforced variability gives a significantly less favorable view of the actual performance of pattern scaling. Using a four-member initial-condition ensemble of HadGEM2-ES simulations, we infer that the root-mean-square errors of pattern-scaled monthly temperature changes over land are less than 0.25°C for global warming up to approximately 3.5°C. Some regional errors are larger than this and, for this GCM, there is a tendency for pattern scaling to underestimate warming over land. For warming above 3.5°C, the pattern-scaled projection errors grow but remain small relative to the climate change signal. We investigate whether patterns diagnosed by pooling GCM experiments from several scenarios are suitable for emulating the GCM under a high-end warming scenario. For global warming up to 3.5°C, pattern scaling using this pooled pattern closely emulates GCM simulations. For warming beyond 3.5°C, pattern-scaling performance is notably improved by using patterns diagnosed only from the high-forcing representative concentration pathway 8.5 (RCP8.5) scenario. Assessments of climate change impacts under high-end warming using pattern-scaling projections could be improved by using change patterns diagnosed from pooled scenarios for projections up to 3.5°C above preindustrial levels and patterns diagnosed from only strong forcing simulations for projecting beyond that. Similar findings are obtained for five other GCMs.