A new version of the general circulation model CNRM-CM has been developed jointly by CNRM-GAME (Centre National de Recherches Météorologiques-Groupe d'études de l'Atmosphère Météorologique) and Cerfacs (Centre Européen de Recherche et de Formation Avancée) in order to contribute to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The purpose of the study is to describe its main features and to provide a preliminary assessment of its mean climatology. CNRM-CM5.1 includes the atmospheric model ARPEGE-Climat (v5.2), the ocean model NEMO (v3.2), the land surface scheme ISBA and the sea ice model GELATO (v5) coupled through the OASIS (v3) system. The main improvements since CMIP3 are the following. Horizontal resolution has been increased both in the atmosphere (from 2.8°to 1.4°) and in the ocean (from 2°t o 1°). The dynamical core of the atmospheric component has been revised. A new radiation scheme has been introduced and the treatments of tropospheric and stratospheric aerosols have been improved. Particular care has been devoted to ensure mass/water conservation in the atmospheric component. The land surface scheme ISBA has been externalised from the atmospheric model through the SURFEX platform and includes new developments such as a parameterization of sub-grid hydrology, a new freezing scheme and a new bulk parameterisation for ocean surface fluxes. The ocean model is based on the state-of-the-art version of NEMO, which has greatly progressed since the OPA8.0 version used in the CMIP3 version of CNRM-CM. Finally, the coupling between the different components through OASIS has also received a particular attention to avoid energy loss and spurious drifts. These developments generally lead to a more realistic representation of the mean recent climate and to a reduction of drifts in a preindustrial integration. The largescale dynamics is generally improved both in the atmosphere and in the ocean, and the bias in mean surface temperature is clearly reduced. However, some flaws remain such as significant precipitation and radiative biases in many regions, or a pronounced drift in three dimensional salinity.

This paper describes the main characteristics of CNRM-CM6-1, the fully coupled atmosphere-ocean general circulation model of sixth generation jointly developed by Centre National de Recherches Météorologiques (CNRM) and Cerfacs for the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6). The paper provides a description of each component of CNRM-CM6-1, including the coupling method and the new online output software. We emphasize where model's components have been updated with respect to the former model version, CNRM-CM5.1. In particular, we highlight major improvements in the representation of atmospheric and land processes. A particular attention has also been devoted to mass and energy conservation in the simulated climate system to limit long-term drifts. The climate simulated by CNRM-CM6-1 is then evaluated using CMIP6 historical and Diagnostic, Evaluation and Characterization of Klima (DECK) experiments in comparison with CMIP5 CNRM-CM5.1 equivalent experiments. Overall, the mean surface biases are of similar magnitude but with different spatial patterns. Deep ocean biases are generally reduced, whereas sea ice is too thin in the Arctic. Although the simulated climate variability remains roughly consistent with CNRM-CM5.1, its sensitivity to rising CO 2 has increased: the equilibrium climate sensitivity is 4.9 K, which is now close to the upper bound of the range estimated from CMIP5 models.

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Globally, thermodynamics explains an increase in atmospheric water vapor with warming of around 7%/°C near to the surface. In contrast, global precipitation and evaporation are constrained by the Earth's energy balance to increase at ∼2-3%/°C. However, this rate of increase is suppressed by rapid atmospheric adjustments in response to greenhouse gases and absorbing aerosols that directly alter the atmospheric energy budget. Rapid adjustments to forcings, cooling effects from scattering aerosol, and observational uncertainty can explain why observed global precipitation responses are currently difficult to detect but are expected to emerge and accelerate as warming increases and aerosol forcing diminishes. Precipitation increases with warming are expected to be smaller over land than ocean due to limitations on moisture convergence, exacerbated by feedbacks and affected by rapid adjustments. Thermodynamic increases in atmospheric moisture fluxes amplify wet and dry events, driving an intensification of precipitation extremes. The rate of intensification can deviate from a simple thermodynamic response due to in-storm and larger-scale feedback processes, while changes in large-scale dynamics and catchment characteristics further modulate the frequency of flooding in response to precipitation increases. Changes in atmospheric circulation in response to radiative forcing and evolving surface temperature patterns are capable of dominating water cycle changes in some regions. Moreover, the direct impact of human activities on the water cycle through water abstraction, irrigation, and land use change is already a significant component of regional water cycle change and is expected to further increase in importance as water demand grows with global population.

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The present study examines the correspondence between short-and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast ensembles from the Transpose Atmospheric Model Intercomparison Project II (Transpose-AMIP II) for July-August 2009 (short term) to the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and AMIP for the JuneAugust mean conditions of the years of 1979-2008 (long term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach.The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations since the atmospheric large-scale states remain close to observations in the first 2-3 days. However, biases associated with model physics can have impacts on the large-scale states by day 5, such as zonal winds, 2-m temperature, and sea level pressure, and the analysis further indicates a good correspondence between shortand long-term biases for these large-scale states. Therefore, improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections.

Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades 1-5 . This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales 6-8 . Discernible human impacts on the Earth's climate system have been revealed by using statistical frameworks of detection-attribution 9-11 . These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, di erent algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets 12,13 , simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm 14,15 . Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts 11 .This study examines the growing season LAI over the NEL (30-75 • N). The LAI is a measurable biophysical parameter using satellite observation, an archived prognostic variable of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ESMs, and a direct indicator of the leaf surface per unit ground area that exchanges energy, water, carbon dioxide and momentum with the planetary boundary layer. We employed the recently published LAI3g data set 12 and the GEOLAND2 LAI data 13 , both of which were quality-controlled over the NEL region for the 1982-2011 period ( Supplementary Information 1). We compared the observed changes of LAI to simulated variations from multi-model results obtained from the CMIP5 archive (Supplementary Information 2 and Supplementary Table 1). These ensemble simulations comprise ALL, with historical anthropogenic and natural forcings, GHG, with greenhouse gases forcing only, NAT, with natural forcing only, CTL, with internal variability (IV) only, esmFixClim2, with CO 2 physiological effects, and esmFdbk2, with greenhouse gases radiative effects. Beyond the standard comparison of time series and patterns of trends, two methods were applied to detect and attribute changes in observed LAI, including a formal 'optimal fingerprint' analysis (Methods).From 1982 to 2011, LAI3g, GEOLAND2 and their mean exhibited greening trends over the NEL vegetated area (85.3%, 69.5% and 80.6%, respectively), except across a narrow latitudinal band over Canada and Alaska, and in a few spots over Eurasia (Fig. 1a-c). The largest positive increase is observ...

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