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PUBLICATIONS Journal of Advances in Modeling Earth Systems RESEARCH ARTICLE 10.1002/2015MS000614 Key Points: Results from the latest Met Office Hadley Centre Climate Model Role of atmospheric resolution on measures of climate response is investigated Many features are robust to changes in resolution

Correspondence to: C. A. Senior, cath.senior@metoffice.gov.uk Citation: Senior, C. A., et al. (2016), Idealized climate change simulations with a high-resolution physical model: HadGEM3-GC2, J. Adv. Model. Earth Syst., 8, 813–830, doi:10.1002/ 2015MS000614. Received 18 DEC 2015 Accepted 2 MAY 2016 Accepted article online 6 MAY 2016 Published online 29 MAY 2016

Idealized climate change simulations with a high-resolution physical model: HadGEM3-GC2 Catherine A. Senior1, Timothy Andrews1, Chantelle Burton1, Robin Chadwick1, Dan Copsey1, Tim Graham1, Pat Hyder1, Laura Jackson1, Ruth McDonald1, Jeff Ridley1, Mark Ringer1, and Yoko Tsushima1 1

Met Office Hadley Centre, Exeter, UK

Abstract Idealized climate change simulations with a new physical climate model, HadGEM3-GC2 from The Met Office Hadley Centre are presented and contrasted with the earlier MOHC model, HadGEM2-ES. The role of atmospheric resolution is also investigated. The Transient Climate Response (TCR) is 1.9 K/2.1 K at N216/N96 and Effective Climate Sensitivity (ECS) is 3.1 K/3.2 K at N216/N96. These are substantially lower than HadGEM2-ES (TCR: 2.5 K; ECS: 4.6 K) arising from a combination of changes in the size of climate feedbacks. While the change in the net cloud feedback between HadGEM3 and HadGEM2 is relatively small, there is a change in sign of its longwave and a strengthening of its shortwave components. At a global scale, there is little impact of the increase in atmospheric resolution on the future climate change signal and even at a broad regional scale, many features are robust including tropical rainfall changes, however, there are some significant exceptions. For the North Atlantic and western Europe, the tripolar pattern of winter storm changes found in most CMIP5 models is little impacted by resolution but for the most intense storms, there is a larger percentage increase in number at higher resolution than at lower resolution. Arctic sea-ice sensitivity shows a larger dependence on resolution than on atmospheric physics.

1. Introduction This paper describes results from a new coupled atmosphere-ocean-sea ice-land model (AOIL) configuration of the Met Office Unified Model (MetUM), HadGEM3-GC2 [Williams et al., 2015]. The model consolidates developments to the physics and dynamics of the MetUM since HadGEM2 [Martin et al., 2011]. Key changes include coupling of the UM atmosphere to new ocean and sea-ice models (NEMO and CICE) [Hewitt et al., 2011], increased vertical levels in the atmosphere (85 compared to 38), substantial changes to the model dynamics (ENDGame) [Wood et al., 2014], and physics such as the PC2 cloud scheme [Wilson et al., 2008a, 2008b] documented in detail in Walters et al. [2011, 2014] and D. N. Walters et al. (The Met Office Unified Model Global Atmosphere 6.0 and JULES Global Land 6.0 configurations, submitted to Geoscientific Model Development, 2016).

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The sixth phase of the coupled model intercomparison project (CMIP) will focus on specific science questions in support of the WCRP Grand Science Challenges and to fill science gaps identified from previous phases of CMIP, notably; How does the Earth System respond to changes in forcing? What are the origins and consequences of systematic model biases? How can we assess future climate changes given climate variability, predictability and uncertainties in scenarios? To address these issues, modeling groups around the world are assembling the most detailed models of the earth-system yet achieved, which require good understanding of the couplings between the increasing numbers of component models. In the UK, a second generation earth-system model, UKESM1 (http://www.jwcrp.org.uk/research-activity/ukesm.asp), is under development that is for the first time being built jointly by the Met Office and the UK academic community. Work on building new earth-system components on top of the coupled system is underway and has been running in parallel to the physical model development program. Coupling of the earth-system components to the physical system is complex and it will take a number of years to deliver a well-tested high-performing model. In the meantime, we have consolidated a suite of physical improvements in atmosphere-only and coupled AOIL model configurations which are already being used within the Met Office unified modeling approach and are now showing benefits for operational short-range and seasonal to decadal prediction

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[Scaife et al., 2014]. Given this, we have run a limited set of present-day climate and climate change studies with HadGEM3-GC2 to investigate the impact of these changes on both the present-day climate and its variability and on the simulation of future climate. A first motivation for this is to characterize the physical model capability and in particular highlight systematic biases which could impact on its performance when coupled to bio-geophysical processes within UKESM1. Results from these first stage experiments will allow two-way interaction with the ESM development running in parallel. This methodology closely follows the proposed continuous model assessment approach being recommended for CMIP6 [Meehl et al., 2014] and the experimental design followed in this paper is essentially a first set of the so-called ‘‘DECK’’ (Diagnosis, Evaluation and Characterisation of Klima) experiments outlined for CMIP6 [Eyring et al., 2016]. A number of authors have shown the benefits of increased atmospheric resolution, for example, on largescale climate processes [e.g., Demory et al., 2014], modes of variability and teleconnections [e.g., Karpechko et al., 2008; Matsueda et al., 2009; Roberts et al., 2009], and regional climate and extremes [e.g., Roberts et al., 2014; Marshall and Scaife, 2010] but only a limited number of coupled AOIL models have been run for future climate at resolutions able to resolve synoptic scale features of the atmosphere. The Met Office in the UK is operationally running the HadGEM3-GC2 model at the higher-resolution configuration (N216) described here for seasonal and decadal forecasts [MacLachlan et al., 2014; Knight et al., 2014] and clear benefits are emerging. Hence, a second key motivation of this work is the assessment of the global and regional climate response to idealized scenarios of increases in greenhouse gases at two resolutions (N216 with 60 km midlatitude grid spacing and N96 with 130 km midlatitude grid spacing). Williams et al. [2015] document the model components of HadGEM3-GC2, provide a description of the coupling, and assess the model performance at the higher resolution (N216) across a range of time scales using metrics of the mean state and its variability. They find that overall HadGEM3-GC2 provides a significant improvement in mean bias and variability over the previous coupled configurations for climate (HadGEM2AO) and seasonal prediction (GloSea4) [Arribas et al., 2011], but note that there are still significant remaining biases. In particular, the warm bias in the Southern Ocean has increased substantially in HadGEM3-GC2 primarily due to the ocean responding differently to the atmospheric flux errors. This remains a first-order problem, given the role of the Southern Ocean in determining the transient climate change and is a target for focused work to reduce the atmospheric flux errors for UKESM1. This paper will describe the first climate change results with HadGEM3-GC2, comparing the results at two different resolutions (N216 and N96) and to those with the previous model HadGEM2-ES. Although the choice of HadGEM2-ES may complicate the comparison as it includes some ES processes (carbon cycle and atmospheric chemistry), Andrews et al. [2012a] document that on the global scale, this has little impact on measures such as Effective Climate Sensitivity (ECS) and Transient Climate Response (TCR). In section 3, we focus on the impact on global measures of climate feedbacks and response, and in section 4, we compare future changes in both the mean and variability of some key large-scale regional responses notably Arcic sea-ice, European circulation and precipitation, and tropical precipitation. This first paper does not aim to detail a complete understanding of all of the mechanistic changes driving differences in the responses as this will be the goal of more detailed targeted future papers, but rather highlights some key global or regional responses that are robust or sensitive to changes in resolution and/or physics and dynamics.

2. Model Description and Experimental Design The HadGEM3-GC2 coupled AOIL model and technical details of the coupling approach are described in detail by Williams et al. [2015]. Hence limited details are given here. The HadGEM3-GC2 configuration is defined by the combination of the component model scientific configurations (Global-Atmosphere (GA)v6.0, Global-Land (GL)v6.0, Global-Ocean (GO)v5.0, Global Sea-Ice (GSi)v6.0), and associated choices about the way these model components are coupled together. The component models are fully documented in the model description sections of Walters et al. (submitted manuscript, 2016), Megann et al. [2014], and Rae et al. [2015]. There are many changes to the physics and dynamics of the MetUM consolidated in the HadGEM3 model since HadGEM2 [Martin et al., 2011]. The UM atmosphere is now coupled to the NEMO ocean and CICE sea-ice models [Hewitt et al., 2011], which relative to HadGEM2 includes much updated ocean physics although the sea-ice physics remains essentially unchanged. HadGEM2 uses a low-resolution 18 parameterized eddy ocean. In the absence of sufficient

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resource to run an eddy resolving resolution, for HadGEM3-GC2, we opt to employ eddy-permitting 1/48 resolution (using NEMO) due to: (1) the dramatic improvement in the representation of boundary and frontal currents, and associated air-sea exchanges and ocean heat/salt transports; (2) inclusion of eddyatmospheric coupling effects, which cannot yet be parameterized; and (3) Southern Ocean circulation response to wind changes. At this resolution, however, eddies are not adequately resolved for much of the ocean poleward of 308N/S [Hallberg, 2013] and in consequence there is an underestimation of EKE (by around 30–40% in midlatitudes) and associated mass, heat, and salt transports. It is not yet possible to parameterize the thickness diffusion impact of missing eddies by Gent McWilliams parameterization in an eddy-permitting model as it damps the permitted eddies, negating the above mentioned benefits. We do however [unlike Griffies et al. 2015] parameterize the missing EKE by employing a small amount of parameterized along-isopycnal diffusion (300 m2 s21). It should, however, be noted that the model climatologies including near-surface biases are quite sensitive to the choice of this parameter value, particularly in the Southern Ocean (where it is known to be a significant term in vertical heat transport budget) [Gregory, 2000]. There are increased vertical levels in the atmosphere (85 compared to 38) and substantial changes to the model dynamics (ENDGame) [Wood et al., 2014]. There are also many changes to the atmospheric physics but some of the most significant include; the new PC2 cloud scheme [Wilson et al., 2008a, 2008b] which uses three prognostic variables for humidity mixing ratio: water vapor, liquid, and ice and a further three prognostic variables for cloud fraction: liquid, ice, and total. The cloud fields can then be modified by shortwave radiation, longwave radiation, boundary layer processes, convection, precipitation, small-scale mixing (cloud erosion), advection, and pressure changes due to large-scale vertical motion. The convection scheme calculates increments to the prognostic liquid and ice water contents by detraining condensate from the convective plume, while the cloud fractions are updated using the nonuniform forcing method of Bushell et al. [2003]. One advantage of the prognostic approach is that clouds can be transported away from where they were created. For example, anvils detrained from convection can persist and be advected downstream long after the convection itself has ceased. Compared to the HadGEM2-ES cloud scheme [Smith, 1990], there is no implicit coupling between cloud fraction and optical depth in PC2 which, for example, allows much larger amounts of very thin cloud—notably high thin cirrus [Williams et al., 2015]; a new Gravity Wave Drag scheme [Vosper, 2015] which represents the effects of low-level flow blocking and the drag associated with stationary gravity waves (mountain waves) based on the scheme described by Lott and Miller [1997]; revisions to the convective entrainment and detrainment rates used in deep convective ascents, inclusion of adaptive detrainment [Derbyshire et al., 2011], and changes to CAPE time scales for deep convection (Walters et al., submitted manuscript, 2016). A 100 year present-day control simulation with forcings from year 2000 (equivalent to experiment 2 from CMIP3) has been completed at N216 and N96 atmospheric resolution coupled to a 0.258 ocean. Where relevant (e.g., for aerosols) emissions vary through the annual cycle. The ocean is initialized from EN3 climatology [Ingleby and Huddleston, 2007]. The top-of-the-atmosphere radiative imbalance in parallel atmosphere-only simulations is 0.8 and 0.6 W m22 (N216/N96) consistent with using present-day forcings; hence, small drifts due to these imbalances are to be expected. Averages (where shown) are taken over the final 50 years of the simulation. Two idealized climate change simulations were initialized from year 36 of the control simulation. In the first, CO2 concentration is increased at 1% per year compounded and run for 140 years up to a quadrupling of CO2 (henceforth referred to as the transient experiment). From this, we determine the model’s TCR, which is defined as the globally averaged temperature change at the time of CO2 doubling (a 20 year average, years 61–80). In the second, CO2 concentrations are abruptly increased to 4XCO2 and run for 150 years (henceforth referred to as the step experiment). From this, we can calculate the ECS [Gregory et al., 2004], an estimation of the equilibrium response of the climate system to a doubling of CO2, assuming that the climate feedbacks remain constant in time. Averages (where used) are taken over the final 50 years of the simulations. Comparison is shown here to experiments with HadGEM2-ES at N96 atmospheric resolution coupled to a 18 ocean [Collins et al., 2011; Andrews et al., 2012a]. In this case, the transient and step experiments were run from a preindustrial control (1860 conditions). Averages (where used) are taken over the final 50 years of the idealized experiments. Tests with HadGEM3-GC2 at N96 resolution have shown no sensitivity to the choice of preindustrial or present day as the initial state for the idealized runs.

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3. Global Climate Response and Feedbacks 3.1. Effective Climate Sensitivity and Response The ECS of HadGEM3-GC2 is 3.1 K/3.2 K at N216/N96 (Figure 1). At this global scale, there is little sensitivity to resolution, however, the ECS of the HadGEM3-GC2 models are substantially lower than HadGEM2-ES (4.6 K). This is not attributable to any one feedback strengthening or weakening but rather a combination of changes in both clear sky and cloud feedbacks (Figure 2). Figure 1. Change in global-annual-mean net radiative flux as a function of global-annual-mean surface-air-temperature change in the step experiments (following Gregory et al. [2004]). Solid lines are linear fits to the 150 years data, following Andrews et al. [2012b]. The Effective Climate Sensitivity is estimated by half of the x intercept, justified since CO2 forcing is approximately logarithmic with concentration, and this experiment is 4XCO2 rather than 2XCO2.

HadGEM2-ES has a net radiative feedback that places it at the top end of the CMIP5 range (Figure 2). Taken individually, none of the clear-sky or cloudy components of the net feedback are significant outliers from the CMIP5 average and indeed mostly fall within the 75th centile of CMIP5 models. However, the relatively strong positive net cloud radiative effect (CRE) feedback, average strength Shortwave (SW) clear-sky feedback, and weak negative Longwave (LW) clearsky feedback combine to give the overall weak negative net feedback, leading to the high values of ECS and TCR (which vary as the inverse of the feedback parameter). In contrast, HadGEM3-GC2 (both resolutions) show comparable if slightly smaller net CRE feedbacks, a slightly strengthened LW clear-sky negative feedback, and a substantially weakened positive SW clear-sky feedback. In combination, these give a strengthened negative net radiative feedback, close to the average of the CMIP5 range, and hence lower values of ECS and TCR. The weakened clear-sky SW feedback in HadGEM3-GC2 arises at least in part from differences at high latitudes (Figure 3). In the Arctic, the polar amplification of the warming is larger in HadGEM2-ES consistent with a larger increase in the total poleward heat transport (see also section 4.1). There is also a larger clear-sky SW feedback in parts of the Southern Ocean likely due to the feedback arising from the greater amount of ice in the HadGEM2-ES control model. The significant Southern Ocean warm-bias in HadGEM3-GC2 produces too little ice in the control.

Figure 2. Global-mean radiative feedback components in W m22 K21 derived from the step experiments for HadGEM3-GC2 N96, HadGEM3-GC2 N216, and HadGEM2-ES and compared to the CMIP5 multimodel ensemble. The feedbacks are (from left to right) shortwave clear-sky, longwave clear-sky, shortwave cloud radiative effect (CRE), longwave CRE, net CRE, and the total (NET) feedback. The grey box plots show the range (max and min), interquartile range, and median values of the feedbacks for the CMIP5 ensemble. Like Figure 1, feedback parameters are calculated following Andrews et al. [2012b], i.e., from the slope of the linear regression of the change in radiative flux against deltaT for the 150 years of the step experiment. The CMIP5 data are calculated in the same way, updated from Andrews et al. [2012b] and Forster et al. [2013].

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To corroborate our attribution of the SW clear-sky feedback changes to physical processes, we make us of the SW Approximate Partial Radiative Perturbation (SW APRP) Technique [Taylor et al., 2007]. SW APRP is a simplified SW radiative transfer model that we calibrate to HadGEM2-ES and HadGEM3-GC2 N96, allowing us to partition the change in

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Figure 3. Geographical distribution of the Shortwave clear-sky feedback in W m22 K21 for (left) HadGEM3-GC2 N96, (middle) HadGEM3-GC2 N216, and (right) HadGEM2-ES. These patterns give the local contribution to the global-mean feedback parameters, i.e., they are determined from the slope of the linear regression of the change in local radiative flux against global-mean deltaT for the 150 years of the step experiment.

SW radiative fluxes into a component from changes in surface albedo, cloud (scattering, absorption plus amount), and noncloud atmospheric scattering plus absorption. We apply the SW APRP technique to monthly mean data and then calculate the feedback via the regression against global-mean dT (as in Figures 1 and 2). The surface albedo feedback in HadGEM2-ES is 0.3 W m22 K21, whereas it is only 0.2 W m22 K21 in HadGEM3-GC2, confirming the smaller surface albedo feedback in HadGEM3-GC2. This difference is smaller than seen in the SW clear-sky feedback because clouds mask underlying changes in surface albedo by reducing the surface luminosity; hence, clear-sky feedbacks overstate the radiative effect of changes in surface albedo [e.g., Soden et al., 2008]. Both models simulate a noncloud atmospheric scattering and absorption feedback (for example, the SW component of the water vapor feedback) of 0.3 W m22 K21. In HadGEM2-ES, the inclusion of interactive vegetation drives differences in dust generation which influence the noncloud atmospheric scattering and clear-sky SW feedback over Australia and parts of Africa [e.g., Andrews et al., 2012a]. However these changes while important locally, largely balance in the global mean and are not a major driver of differences in clear-sky SW. The net CRE feedback changes little between the three models but, interestingly, the SW and LW components are toward the higher and lower ends of the range of CMIP5 models, respectively, in the HadGEM3GC2 experiments compared to HadGEM2-ES. The global LW CRE feedback changes from weakly positive to negative and the SW CRE feedback is more strongly positive (Figure 2). The pattern of cloud radiative response in the two GC2 models (Figure 4) shows a strong positive LW CRE feedback balanced by a strong negative SW CRE feedback across the tropical pacific and a horse-shoe pattern of the opposite sign response to the north and south of this and across the maritime continent. This is related to the enhanced warming in the East Pacific seen in the GC2 models (Figure 4, bottom row) that resembles an El Nino-like response. The opposing changes in the LW and SW components of the CRE are associated with changes in the response of high cloud, which are well known to have large but opposing LW and SW effects. In contrast, HadGEM2-ES shows a weaker cloud radiative response over the tropical pacific and maritime continent, consistent with the weaker changes seen in the global mean response. For the HadGEM3-GC2 runs, optically active high-top clouds reduce over the maritime continent but increase over the tropical Pacific giving globally an overall largely neutral response in high-top cloud which changes little with increasing temperature (Figure 5, left column, first three rows). For subvisual high-top cloud, there are increases right across the tropics which get bigger at warmer temperatures (Figure 5, left column, bottom row). For HadGEM2-ES, high-top optically active cloud increases globally with temperature (Figure 5, left column, first three rows), as increases over the tropical pacific dominate smaller reductions over the maritime continent. Conversely, for subvisual high-top cloud, the reductions over the maritime continent dominate the global mean (Figure 5, left column, bottom row). The cause of the different behavior of high-top cloud over the maritime continent is difficult to attribute to any one of the multiple changes implemented in HadGEM3-GC3 relative to HadGEM2-ES. Notably, the new model includes a new cloud scheme (PC2) [Wilson et al., 2008a,b], but there are also changes to convection, enhanced vertical resolution, and the dynamical changes introduced with ENDGame. Preliminary runs reverting to the HadGEM2-ES cloud scheme suggest that the new cloud scheme is not the cause of either the larger amounts of subvisual cirrus seen in HadGEM3-GC2 or consequently the different high cloud

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Figure 4. Geographical distribution of the cloud feedback components ((first row) LW, (second row) SW, and (third row) Net) in W m22 K21 and (fourth row) surface-air-temperature change pattern in K K21, as defined in Figure 3, for (first column) HadGEM3-GC2 N96, (second column) HadGEM3-GC2 N216, and (third column) HadGEM2-ES.

feedbacks. Runs with uniform 14K SST increases also show this behavior, suggesting that the different pattern of ocean response to warming (Figure 4, bottom row) is not directly driving the cloud behavior either, suggesting there may be complex feedbacks between the atmospheric physics and ocean responses. The sign of changes in low and midlevel cloud is more consistent across the models (Figure 5 right column for low cloud), although the changes in medium-thickness lower level cloud differ in size, notably over the trade cumulus regions and the Southern Ocean (not shown). This similarity in low cloud response is perhaps initially surprising given the dominance of low cloud in driving uncertainty in multimodel ranges of cloud feedbacks [e.g., Bony and Dufresne, 2005] but it is noteworthy that there have been no major changes to

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Figure 5. Change in global-annual-mean ISCCP cloud types as a function of global-annual-mean surface-air-temperature change from 150 years of the step experiments. ISCCP cloud types defined as output from the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) [Bodas-Salcedo et al., 2011], which produces model diagnostics that emulate the International Satellite Cloud Climatology Project (ISCCP) [Rossow and Schiffer, 1999] as a function of Cloud Top Pressure (CTP) in MB and optical thickness, s. (left column) High-top clouds (50 < CTP < 440) and (right column) low-top clouds (680 < CTP < 1000); (first row) thick cloud (23 < s < 379), (second row) medium cloud (3:6 < s < 23), (third row) thin cloud (0:3 < s < 3:6), and (fourth row) subvisual cloud (s < 0:3).

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Figure 6. Global-mean surface-air-temperature response (K) over 80 years of the transient experiment: (blue) HadGEM3-GC2 N96, (red) HadGEM3-GC2 N216, (black) HadGEM2-ES, and (grey) CMIP5 AOGCMs. The box whisper plot shows the min, max, median, and upper/lower quartile range of the CMIP5 TCRs. Overlaid circles indicate the TCR of the models described here.

The impact of atmospheric horizontal resolution even for the response of the different cloud types is again very small compared to the impact of the physics changes between HadGEM2ES and HadGEM3-GC2. We speculate that the difference in the atmospheric resolution here (130–60 km) is still too coarse to impact on the behavior at the cloud scale but that we might expect to see more fundamental impacts, e.g., at convective-permitting resolutions.

The resulting pattern of temperature and cloud responses in HadGEM3-GC2 is very similar to the CMIP5 multimodel long-term mean response [e.g., Andrews et al., 2015] In contrast, HadGEM2-ES shows a pattern of response in SSTs and cloud more similar to the short-term response (years 1–20) (Figure 4, bottom row) with notable warming in the trade cumulus regions likely arising from the strong low cloud response there. 3.2. Transient Climate Response The TCR is 1.9 K/2.1 K at N216/N96 in HadGEM3-GC2 compared to 2.5 K in HadGEM2-ES (Figure 6). Relative to HadGEM2-ES, the TCR in HadGEM3-GC2 is larger than would be expected from simply the ratio of the climate sensitivities and the overall ocean heat uptake is 16% smaller in the HadGEM3-GC2 experiments than in HadGEM2-ES. The warming of the deep ocean is larger in HadGEM2-ES consistent with an overall larger ocean heat uptake and vertical transport of heat compared to both of the HadGEM3-GC2 experiments which show no sensitivity to the atmospheric resolution (Figure 7, top). The heat uptake is controlled in part by mesoscale eddies [Griffies et al., 2015], which are wholly parameterized in a 18 resolution ocean (HadGEM2-ES) and explicitly permitted at 0.258 resolution (HadGEM3-GC2). The parameterized eddies tend to be more efficient at vertical heat transports, including along isopycnal mixing, than when eddies are permitted but not fully resolved by the ocean physics. This is consistent with the relatively faster spin-up of the 18 ocean. Regionally, the largest difference in ocean heat uptake between HadGEM2-ES and the HadGEM3-GC2 experiments occurs in the Atlantic at depth (Figure 9). There are also relatively smaller Southern Ocean SST biases in HadGEM2-ES in the presence of similar sized atmospheric flux errors suggesting differences in ocean lateral and/or vertical heat transports between the two models (e.g., Figure 7, bottom). The Atlantic Meridional Overturning Circulation (AMOC) shows a reduction during the transient experiment in all three model configurations (Figure 8) consistent with all CMIP5 models and scenarios. Again, there seems little impact of atmospheric resolution but the AMOC reduction in the HadGEM3-GC2 experiments is larger than in HadGEM2-ES. This results in a larger reduction in ocean heat transport in to the North Atlantic in the HadGEM3-GC2 models which is not evident in HadGEM2-ES where the ocean heat advection out of the region is generally smaller than in the HadGEM3-GC2 models (Figure 9, right).

4. Regional Responses In this section, we highlight the impact of the latest model for some key regional responses in the mean and variability focusing on aspects where we have found or expected horizontal resolution to have an impact (e.g., Arctic sea-ice, European storms) or where we have found robustness in spite of multiple

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Figure 7. (top row) Hovmuller plot of global mean ocean vertical heating in 140 years of the transient experiments: (left) HadGEM3-GC2 N96, (middle) HadGEM3-GC2 N216, and (right) HadGEM2-ES. (bottom row) Zonal mean surface heat flux biases difference from 1985 to 2012 combined satellite/reanalysis data product [Liu et al., 2015] in atmosphere-only simulations in W m22 (blue) HadGEM3-A-N96, (red) HadGEM3-A-N216, and (black) HadGEM2-A.

changes between the new and old models (tropical precipitation). We point to potential mechanisms although a full understanding of the drivers of all mechanisms is beyond the scope of this paper. 4.1. Arctic Response Arctic sea ice area reaches its annual minimum in September, and the decline of September sea-ice with increased CO2, indicates when the Arctic Ocean first becomes ice free (Figure 10, top). The ice is initially more extensive in HadGEM3-GC2 at N96 than N216, a characteristic which may be attributed to a 28C warmer regional temperature in the control simulation at N216. However, the Arctic mean 1.5 m temperature in the HadGEM2-ES control is nearly identical to that of HadGEM3-GC2 at N96 yet the simulation has a lower initial ice area, much more similar to the warmer N216 HadGEM3-GC2 simulation. This provides an indication of the limited extent that sea-ice thermodynamic parameter tuning (albedo and thermal conductivity) in HadGEM2-ES and HadGEM3-GC2 can influence the sea-ice area [Rae et al., 2015]. The surface winds

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Figure 8. Change in Atlantic Meridional Overturning Circulation (AMOC) (Sv) in 140 years of the transient experiments: (blue) HadGEM3-GC2 N96, (red) HadGEM3-GC2 N216, and (black) HadGEM2-ES.

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drive the sea ice dynamics, convergence, and export from the Arctic and this ice motion is weakly constrained by tuning. Differences in the surface wind climatology, and associated atmospheric heat transport, for different model configurations and resolutions can lead to changes in the sea ice characteristics. The sea ice parameters are not separately tuned at the different HadGEM3-GC2 resolutions. Tuning of such parameters affects the equilibrium sea ice climatology, with initial area and thickness impacting on the TCR [Hodson et al., 2013]. With the 28C colder global climate at N96 producing thicker and more extensive sea ice, this may provide part of the reason for a delayed sea ice decline at N96 (Figure 10, top).

The sensitivity of Arctic sea ice area to global temperature is linear for all climate models [Ridley et al., 2007], and for a single model, independent of climate scenario forcing [Gregory et al., 2002]. Thus, the climate sensitivity of Arctic sea ice can be expressed as a single number, the gradient of the linear fit to the percentage change in total ice area against change in global mean temperature. The sensitivity for HadGEM2-ES is 214 6 0.2% K21 with a statistically identical value for HadGEM3-GC2 at N96.

Figure 9. Change in Ocean Heat Uptake and heat divergence converted into equivalent surface heat flux (W m22) in the transient experiments. (left column) Heat uptake for upper ocean, (middle column) heat uptake for deep ocean, and (right column) heat divergence. (top row) HadGEM3-GC2 (N96); (middle row) HadGEM3-GC2 (N216); (bottom row) HadGEM2ES.

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Figure 10. Arctic sea-ice response. (top) September sea ice area for each of the models, from 140 years of the transient experiments; (middle) Arctic sea ice change as a function of global temperature rise in the transient experiments. The gradients of the linear least squares fit attached to the legend (one sigma error in gradients at 0.2% K21); (bottom) change in atmospheric heat flux transport averaged over Arctic sea-ice in W m22.

However, we find an increased sensitivity in HadGEM3-GC2 at N216 of 217 6 0.2% K 21 (Figure 10, middle). The Arctic sea ice response to forcing thus appears more dependent on atmospheric model resolution than differences in model physics or on the sea-ice area in the control simulation.

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Figure 11. North Atlantic and European winter (50 year DJF mean) precipitation response (mm d21) for step minus control for (left) HadGEM3-GC2 N96, (middle) HadGEM3-GC2 N216, and (right) HadGEM2-ES.

The impact of resolution on sea ice climate sensitivity occurs across the Siberian shelf-seas in summer (not shown). We calculate the annual mean heat transport flux as the average flux difference in the TOA and surface fluxes over Arctic sea-ice and find that it is statistically the same for each of the control simulations at an equivalent TOA flux of 112 6 1 W m22. The Arctic Ocean annual mean atmospheric heat transport flux anomalies and sea ice area anomalies are strongly correlated with a coefficient of 0.86. Although poleward heat transport fluxes for HadGEM2-ES and HadGEM3-GC2 N216 increase throughout the 1% scenario, that for HadGEM3-GC2 N96 remains static for the first 100 years (Figure 10, bottom), leading to the delay in sea ice decline at N96 and the difference in sea ice climate sensitivity relative to N216. 4.2. European Precipitation and Storminess Scaife et al. [2014] and MacLachlan et al. [2014] describe the benefits of increased resolution for seasonal predictability of the NAO and precipitation forecasting for the UK and Europe in an earlier HadGEM3 model configuration. Their comparison is N216 atmospheric resolution with 85 vertical levels, coupled to a 0.258 ocean against the previous GLOSEA4 seasonal system that used an atmospheric resolution of N96 and 38 vertical levels coupled to a 18 ocean (as used in HadGEM2-ES). They attribute the improvements to reduced biases in the North Atlantic, arising mainly from the increased ocean resolution and better stratospherictropospheric forced teleconnections to the NAO, leading to a better winter Atlantic blocking climatology. Williams et al. [2015] find a small further improvement in the spatial correlation of the NAO pattern in HadGEM3-GC2 at N216 resolution compared to the earlier version of the coupled HadGEM3 model used by Scaife et al. [2014] and MacLachlan et al. [2014]. Hence here we address the question of whether these advances found in seasonal predictability have any impact on the future climate change signal in precipitation and storminess over the UK and Europe. The winter precipitation response (Figure 11) shows that the HadGEM3-GC2 models at both horizontal resolutions and HadGEM2-ES project increased precipitation over the UK and northern Europe with drying over the Mediterranean. HadGEM3-GC2 at N216 has broad increases in precipitation over the mid-North Atlantic and reductions to the south whereas there are some reductions over the eastern North Atlantic in N96. The precipitation change in both HadGEM3-GC2-models is consistent with a tripolar response pattern over Europe in storm track density (Figure 12) seen in most CMIP5 models [Zappa et al., 2013] with an increase in storminess over the UK but reductions over the Mediterranean and in the Norwegian Sea. At N216, there is a somewhat more pronounced eastward extension of the North Atlantic storm track compared to the lower resolution (Figure 12) consistent with a larger rainfall increase in the mid-Atlantic. The response in the step experiments is similar if somewhat larger than the HadGEM2-ES response in RCP4.5 [Zappa et al., 2013, Figure 1 of supplementary material] although a direct comparison of tracked storms in HadGEM2-ES step experiment has not been possible. However, an analysis of band-pass filtered 500 mb height statistics (not shown) suggests that the eastward extension of the storm track is not evident in HadGEM2-ES. McDonald [2011] relates such an extension to a minimum in SST in the North Atlantic that is seen in the HadGEM3-GC2 experiments but not in HadGEM2-ES (Figure 4, bottom) and is associated with the greater reduction in the AMOC (see section 3). The total number of storms over the North Atlantic increases significantly at N216 compared to N96 in HadGEM3-GC2 (Figure 13, left columns, top) and is in better agreement with ERA-Interim. Both resolutions show a significant reduction in the total number of storms in the step experiments (Figure 13, left columns, bottom). However, when

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Figure 12. North Atlantic and European winter (50 year DJF mean) cyclone track density response (tracks per 106 km2/month) for step minus control for HadGEM3-GC2 (left) N96 and (right) N216. Cyclones are tracked relative vorticity on 850 hPa data at six hourly intervals using an objective technique (see Hodges [1994, 1995, 1996] and Hoskins and Hodges [2002]). The tracking is performed at a spectral resolution of T42 on a Gaussian grid so that synoptic-scale features can easily be identified. The data are spatially filtered by removing a background field. This is done by setting the coefficients in the spherical harmonic expansion of the field at each time step to zero for total wave numbers n 5. The filtered fields are equivalent to the 5 < n 42 component of the original fields. Individual cyclones are tracked as features in the filtered fields. Only those features with a magnitude greater than 1025 s21, in the filtered fields, are tracked and the cyclone tracks must last for at least 2 days and move a distance of 1000 km. The intensity of the cyclones used in Figure 13 is the filtered 850 hPa relative vorticity used for tracking. The black box indicates the region used for Figure 13.

considering only the most intense storms, the N216 model shows a better agreement with the reanalysis for the present-day control than N96 (Figure 13, right columns, top). The percentage increase in the number of most intense storms in the N216 step experiment is significant and larger than at N96 which is not significant (Figure 13, right columns, bottom). An important conclusion then is that the general pattern of change in the winter precipitation and North Atlantic storm track seems to be robust to significant changes in atmospheric physics and an increase in the horizontal and vertical resolution of the atmospheric model, although notably for the UK, there are regional details that depend on both. Nonetheless, these changes in storms and rainfall are certainly within the intermodel spread from CMIP5 [Zappa et al., 2013]. However, when considering the most intense storms which are better captured at the higher resolution, the HadGEM3-GC2 model suggests a larger percentage increase in number at 4XCO2 than at the lower resolution. 4.3. Tropical Response We use the methods of Chadwick et al. [2013] to consider drivers of tropical rainfall change at the two resolutions of HadGEM3-GC2 and to compare to HadGEM2-ES. The two resolutions of HadGEM3-GC2 have remarkably similar patterns of precipitation change in the step experiments (Figures 14 and 15), particularly over land. The main difference at N216 is a slightly amplified gradient of precipitation change in the tropical Atlantic Ocean, and a reduced gradient in the tropical Indian Ocean. In both ocean basins, this involves a negative NW, positive SE precipitation anomaly dipole of rainfall change between the two resolutions. These differences in precipitation change are reflected in similar differences in the patterns of climatological precipitation between the two resolutions in these two ocean basins, but with the opposite sign (i.e., positive NW, negative SE). Therefore, this difference in the precipitation change anomalies could simply reflect the fact that there is more climatological precipitation present at N216 to be moved around, and/or may be linked to a common process in both ocean basins that varies with resolution. HadGEM3-GC2 precipitation changes are slightly smaller in the tropical mean than HadGEM2-ES (Figure 14, left column), consistent with a smaller climate sensitivity, but this difference is much smaller for precipitation change than it is for temperature change due to the offsetting effect of the weakening tropical circulation. Regional differences are most pronounced over the tropical oceans and South America. These are due to different patterns of convective shifts (Figure 14, right column), which in many regions are consistent with differences in SST gradient change. In particular, HadGEM3-GC2 warms less than HadGEM2-ES in the

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Figure 13. (top) Frequency of North Atlantic winter cyclones (50 year DJF mean) for ERA interim (ERAI, green), and HadGEM3-GC2 N96 (blue) and N216 (red) for controls and (bottom) percentage change in step-control. The left columns are for all cyclones and the right columns are for the most intense cyclones (maximum filtered relative vorticity along track center greater than 11 3 1025 s21). To be included, the cyclone track must intersect the region shown on Figure 12. The ERAI tracks were provided by Kevin Hodges [Hodges et al., 2011] and were derived from ECMWF ERA-Interim reanalysis data [Dee et al., 2011] for the period 1980–2009. The vertical bars on the top figure indicate 6one standard deviation about the mean. KS tests indicate that the N96 and ERAI storms are from different distributions at the 5% level, that the N216 and ERAI distributions are the same, and that the N96 and N216 distributions are different at the 5% level. For the bottom figure, the vertical bars show the 90% confidence interval as given by a bootstrap method where 1000 pairs of samples of length 50 year were taken, with replacement, from 139 year of the control simulations. KS tests show that the distribution of storms in N96 step, N216 step, and the intense storms in N216 step are all different from their respective control distributions at the 5% level.

equatorial Pacific cold tongue and Arabian Sea, and this is consistent with differences in DP in these regions. These differences in DSST between the models may be associated with reduced cold biases in HadGEM3-GC2 in the cold tongue and Arabian Sea, which have been shown to influence future climate projections [Levine et al., 2013]. The reduced biases are likely to be due to a combination of various model changes, including the new NEMO ocean model, and atmospheric physics (e.g., convection) and dynamics changes that have led to reduced surface wind stress in these regions. Over some regions, differences may also be partly due to differences in the climatological rainfall fields. The Atlantic ITCZ is further south in HadGEM3-GC2 than in HadGEM2, and this may contribute to a stronger northward shift of the ITCZ rainfall toward the equator over both the tropical Atlantic and tropical South America, due to there being more rainfall available to be shifted northward. This difference in the climatological position of the ITCZ may be related to a large southern ocean warm bias in HadGEM3-GC2, which alters the inter-hemispheric energy balance of the model [Haywood et al., 2013]. The exact reason for the increased warm bias from HadGEM2-ES to HadGEM3-GC2 remains unclear, but it is likely to be influenced by the new NEMO ocean and atmospheric physics and dynamics changes that affect air-sea fluxes. Over land, other than in parts of South America, there is little difference between the HadGEM3-GC2 and HadGEM2-ES precipitation change patterns (Figure 15). There is a significant difference in percentage rainfall change over India, but because of the large negative rainfall bias over this region, this corresponds to only quite a small absolute difference in rainfall change (this is also true over desert regions). Overall over

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Figure 14. Tropical precipitation response (mm d21, colors) step-control. Line contours show control precipitation (mm d21). (left column) dP; (right column) dPshift is the component of precipitation change associated with spatial shifts in convection. (top row) HadGEM3-GC2 N96, (middle row) HadGEM3-GC2 N216, and (bottom row) HadGEM2-ES.

land, the similarity between the models at different resolutions and with substantially different dynamics and physics is relatively small given the wide variations in CMIP5 models [Kent et al., 2015].

5. Summary A new Met Office Hadley Centre Climate model, HadGEM3-GC2, has been run for idealized future climate experiments at two atmospheric resolutions, N216 and N96. Measures of global mean climate response (ECS and TCR) have been assessed at the two resolutions and compared to those of the previous model, HadGEM2-ES. We find substantial differences between HadGEM2-ES and HadGEM3-GC2 model versions but little impact of resolution at the global scale. Both ECS and TCR are lower in HadGEM3-GC2 than in HadGEM2-ES. The lower ECS arises from a combination of weakened positive (SW clear-sky) and strengthened negative (LW clear-sky) feedbacks in the new model. The weakened clear-sky SW feedback in HadGEM3-GC2 arises at least in part from a weaker surface albedo feedback mainly in high latitudes. The global-mean net cloud feedback in HadGEM3-GC2 remains similar to HadGEM2-ES but arises from much larger but canceling SW and LW components. Notably, these now lie on the extreme end of the CMIP5 ranges. Further work is needed to attribute the different response, especially of high thin cloud, to any one of the multiple changes implemented in HadGEM3-GC3 relative to HadGEM2-ES. The lower TCR in HadGEM3-GC2 is largely consistent with the lower ECS relative to HadGEM2ES. However, a smaller ocean heat uptake in HadGEM3-GC2 ameliorates this effect somewhat and the TCR in HadGEM3-GC2 is larger than the simple ratio of the ECS would suggest. We speculate that this arises from a change in the vertical mixing between HadGEM2-ES and HadGEM3-GC2. Both measures of global climate response are remarkably robust to changes in atmospheric resolution. The regional climate sensitivity of Arctic sea ice does show a sensitivity to atmospheric resolution with the HadGEM3-GC2 N216 model showing a stronger percentage reduction in ice area per degree warming than

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at N96 related to a larger poleward heat transport into the Arctic at higher resolution. The sensitivity in HadGEM2ES and HadGEM3-GC2 at the same atmospheric resolution (N96) is identical suggesting a stronger dependence on atmospheric resolution than the (substantial) physics and dynamics changes across these models, although we should interpret this with caution without multiple ensemble members of transient runs for the different models to improve significance. HadGEM3-GC2 at N216 is being used for seasonal and decadal prediction and has shown significant predictable skill in simulating the NAO. Here we find little impact of increased atmospheric resolutions on the continentalscale signal in winter North Atlantic storminess under climate change, which importantly appears robust across both physics and resolution changes. However, there are more substantial local differences including a more noticeable Figure 15. Percentage precipitation change over tropical land for step-control. 21 eastward extension of the storm track The line contour of 0.55 mm d indicates a nominal desert threshold. (top) HadGEM3-GC2 N96, (middle) HadGEM3-GC2 N216, and (bottom) HadGEM2-ES. and related precipitation increases on the western fringes of Europe including the UK. Most notably, the strongest storms show a larger percentage increase in number under 4XCO2 conditions at the higher resolution than at N96. Acknowledgment We would like to thank three anonymous reviewers for their thoughtful and helpful reviews which have improved the paper considerably. We thank Kevin Hodges for the use of his TRACK package and his ERA-Interim cyclone tracks, and ECMWF for the ERA-Interim reanalysis data. We acknowledge support from the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). The source code for the models used in this study, MetUM, Jules, and NEMO are free to use. To apply for a license for MetUM, go to http://www.metoffice.gov.uk/research/ collaboration/um-collaboration, and for permission to use JULES, go to https://jules.jchmr.org. NEMO is available to download from www. nemo-ocean.eu. For more information on the exact model versions and branches applied, please contact the authors. HadGEM2-ES data are available from the CMIP5 data archive (http://cmip-pcmdi.llnl.gov/cmip5/). HadGEM3-GC2 model data are archived at the Centre for Atmospheric Data, and are currently available to UM partners.

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The pattern of tropical precipitation changes in response to climate forcing appears remarkably robust across all three model versions, particularly over land, with the magnitude of response generally slightly smaller in the HadGEM3-GC2 runs than in HadGEM2-ES, consistent with a lower climate sensitivity. However, there are some regional differences that are most pronounced over the South tropical Pacific and South Indian oceans and these may be related to improvements in systematic biases in land or ocean surface temperatures or differences in the climatological rainfall between the models. HadGEM3-GC2 is the latest model from the Met Office Hadley Centre and contains many of the physics and dynamics developments that will be consolidated into the physical model that will be the basis for UKESM1, the UK contribution to CMIP6. This paper has documented some initial findings from idealized climate change experiments with HadGEM3-GC2 and we have highlighted some sensitive and other robust responses to changes in atmospheric resolution, physics, and dynamics. The change in global climate sensitivity has arisen more from changes in clear-sky feedbacks than from clouds with surprisingly little sensitivity to the simulation of low cloud in contrast to findings of many previous studies. Further work is needed to understand the mechanisms driving some of the differences, e.g., in the response of tropical high cloud or the AMOC. Building on this knowledge will provide a sound physical basis for understanding both future projections and the role of earth-system processes in UKESM1.

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