Perturbed physics configurations of the HadAM3 atmospheric model driven with observed sea surface temperatures (SST) and sea ice were tuned to outgoing radiation observations using a Gauss-Newton line-search optimisation algorithm to adjust the model parameters. Four key parameters, that previous research found affected climate sensitivity, were adjusted to several different target values including two sets of observations. The observations used were the global average Reflected Shortwave Radiation (RSR) and Outgoing Longwave Radiation (OLR) from the Clouds and Earth's Radiant Energy System instruments combined with observations of ocean heat content. Using the same method, configurations were also generated that were consistent with the earlier Earth Radiation Budget Experiment results. Many, though not all, tuning experiments were successful with about 2500 configurations being generated and the changes in simulated outgoing radiation largely due to changes in clouds. Clear sky radiation changes were small largely due to a cancellation between changes in upper-tropospheric relative humidity and temperature. Changes in other climate variables are strongly related to changes in OLR and RSR particularly on large scales. There appears to be some equifinality with different parameter configurations producing OLR and RSR values close to observed values. These models have small differences in their climatology with the one group being similar to the standard configuration and the other group drier in the tropics and warmer everywhere.

Five stakeholder-relevant indices of agro-meteorological change were analysed for the UK, over past (1961-1990) and future (2061-2090) periods. Accumulated Frosts, Dry Days, Growing Season Length, Plant Heat Stress and Start of Field Operations were calculated from the E-Obs (European Observational) and HadRM3 (Hadley Regional Climate Model) PPE (perturbed physics ensemble) data sets. Indices were compared directly and examined for current and future uncertainty. Biases are quantified in terms of ensemble member climate sensitivity and regional aggregation. Maps of spatial change then provide an appropriate metric for end-users both in terms of their requirements and statistical robustness. A future UK is described with fewer frosts, fewer years with a large number of frosts, an earlier start to field operations (e.g., tillage), fewer occurrences of sporadic rainfall, more instances of high temperatures (in both the mean and upper range), and a much longer growing season. 1 Introduction Decision making for climate change adaptation within the land use sector, and appropriate policy development, requires consideration of relevant environmental pressures (Matthews et al. 2008). The variability of key weather-driven factors, thresholds and tolerances must be identified to form the basis for land manager decision-making (Matthews et al. 2008; McCrum et al. 2009; Rivington et al. 2008a, 2013). In the UK 70 % of land-use is agricultural (DEFRA 2011), only 6.8 % can be classified as "urban" (NEA 2012). When in-urban "greenspaces" and water surfaces are accounted for, the UK's built environment drops to roughly 2.3 % of landuse, with less than 0.5 % for Scotland (NEA 2012). Land management invested individuals and agencies are thus highly exposed to environmental change (Kane et al. 1992). Prior studies have focussed largely on metrics of drought (Keyantash and Dracup 2002; Piao et al. 2010) as the most costly form of discrete climate impact (Keyantash and Dracup

A large number of perturbed-physics simulations of the HadAM3 atmospheric model were compared with the CERES (Clouds and Earth's Radiant Energy System) estimates of Outgoing Longwave Radiation (OLR) and Reflected Shortwave Radiation (RSR) as well as OLR and RSR from the earlier ERBE (Earth Radiation Budget Experiment) estimates. The model configurations were produced from several independent optimisation experiments in which four parameters were adjusted. Model-observation uncertainty was estimated by combining uncertainty arising from: satellite measurements, observational radiation imbalance, total solar irradiance, radiative forcing, natural aerosol, internal climate variability, Sea Surface Temperature and that arising from parameters we did not vary. Using an emulator built from 14,001 "slab" model evaluations carried out using the climateprediction.net ensemble the climate sensitivity for each configuration was estimated. Combining different prior probabilities for model configurations with the likelihood for each configuration, and taking account of uncertainty in the emulated climate sensitivity gives, for the HadAM3 model, a 2.5-97.5% range for climate sensitivity of 2.7-4.2 K if the CERES observations are correct. If the ERBE observations are correct then they suggest a larger range, for HadAM3, of 2.8-5.6 K. Amplifying the CERES observational covariance estimate by a factor of 20 brings CERES and ERBE estimates into agreement. In this case the climate sensitivity range is 2.7-5.4 K. Our results rule out, at the 2.5 % level, for HadAM3 and several different prior assumptions climate sensitivies greater than 5.6 K.

The EISMINT II experiments revealed the tendency for idealized model ice sheets to produce spatially variable flow under certain uniform thermal, mass-balance and topographic boundary conditions. Warm, fast-flowing streams with enhanced creep were separated by zones of colder, slower flow. Similar but different spatial patterns of differentiated flow were produced by all authors. We present further experiments that explore the formation and function of such ice streams at higher modelled resolutions. These are explored by the use of flat, but stochastically rough (10 m amplitude) beds, idealized, parallel-sided model ice sheets and models of finer (12.5 and 5 km) resolutions. Ice streams self-organize irregularly, but with consistent typical spacings which vary with thermal and miss-balance boundary conditions. More radial features are produced at finer scales indicating a dependency on the grid resolution used although this is not linear; at finer resolutions streams occupy increasingly more gridcells. This variation in scale may be related to the finer resolution of the warm/cold streaming/non-streaming boundary. The numerical solution of the thermodynamic ice equation is also highly sensitive to the orthogonality of the model grid. A major deficiency is that the numerical solution appears to fail where the flow is parallel to the grid axes, suggesting that artificial diffusion in the numerical scheme helps to smooth streams lying across the axes directions. The inclusion of sliding produces fewer, more concentrated, flow features, but these also display a level of scale-dependent organization. The spatial arrangement of such streams adjusts in response to the global mass flux of the ice sheet between "warm" and "cold" flow end-member. The results point to a mechanism in which ice sheets respond to climate by altering the large-scale arrangement of their flow patterns.

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This study investigates the potential influences of anthropogenic forcings and natural variability on the risk of summer extreme temperatures over China. We use three multi-thousand-member ensemble simulations with different forcings (with or without anthropogenic greenhouse gases and aerosol emissions) to evaluate the human impact, and with sea surface temperature patterns from three different years around the El Niño-Southern Oscillation (ENSO) 2015/16 event (years 2014, 2015 and 2016) to evaluate the impact of natural variability. A generalized extreme value (GEV) distribution is used to fit the ensemble results. Based on these model results, we find that, during the peak of ENSO (2015), daytime extreme temperatures are smaller over the central China region compared to a normal year (2014). During 2016, the risk of nighttime extreme temperatures is largely increased over the eastern coastal region. Both anomalies are of the same magnitude as the anthropogenic influence. Thus, ENSO can amplify or counterbalance (at a regional and annual scale) anthropogenic effects on extreme summer temperatures over China. Changes are mainly due to changes in the GEV location parameter. Thus, anomalies are due to a shift in the distributions and not to a change in temperature variability.

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