This study presents the global climate model IPSL‐CM6A‐LR developed at IPSL to study natural climate variability and climate response to natural and anthropogenic forcings as part of the 6^th phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double ITCZ, frequency of midlatitude wintertime blockings, and ENSO dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL‐CM5A‐LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850‐2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed.

This paper documents a global Bayesian variational inversion of CO₂ surface fluxes during the period 1988–2008. Weekly fluxes are estimated on a 3.75° × 2.5° (longitude‐latitude) grid throughout the 21 years. The assimilated observations include 128 station records from three large data sets of surface CO₂ mixing ratio measurements. A Monte Carlo approach rigorously quantifies the theoretical uncertainty of the inverted fluxes at various space and time scales, which is particularly important for proper interpretation of the inverted fluxes. Fluxes are evaluated indirectly against two independent CO₂ vertical profile data sets constructed from aircraft measurements in the boundary layer and in the free troposphere. The skill of the inversion is evaluated by the improvement brought over a simple benchmark flux estimation based on the observed atmospheric growth rate. Our error analysis indicates that the carbon budget from the inversion should be more accurate than the a priori carbon budget by 20% to 60% for terrestrial fluxes aggregated at the scale of subcontinental regions in the Northern Hemisphere and over a year, but the inversion cannot clearly distinguish between the regional carbon budgets within a continent. On the basis of the independent observations, the inversion is seen to improve the fluxes compared to the benchmark: the atmospheric simulation of CO₂ with the Bayesian inversion method is better by about 1 ppm than the benchmark in the free troposphere, despite possible systematic transport errors. The inversion achieves this improvement by changing the regional fluxes over land at the seasonal and at the interannual time scales.

[1] Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite‐based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddy‐covariance flux measurements, and atmospheric water balance datasets. The LandFlux‐EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations‐based products. Though differently constrained with observations, the analyzed reference datasets display similar large‐scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter‐product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations.

Accurate assessment of anthropogenic carbon dioxide (CO₂) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO₂ emissions from fossil-fuel combustion and cement production (E_FF) are based on energy statistics, while emissions from land-use change (E_LUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO₂ concentration is measured directly and its rate of growth (G_ATM) is computed from the annual changes in concentration. The mean ocean CO₂ sink (S_OCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S_OCEAN is evaluated for the first time in this budget with data products based on surveys of ocean CO₂ measurements. The global residual terrestrial CO₂ sink (S_LAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO₂ and land cover change (some including nitrogen–carbon interactions). All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2003–2012), E_FF was 8.6 ± 0.4 GtC yr⁻¹, E_LUC 0.9 ± 0.5 GtC yr⁻¹, G_ATM 4.3 ± 0.1 GtC yr⁻¹, S_OCEAN 2.5 ± 0.5 GtC yr⁻¹, and S_LAND 2.8 ± 0.8 GtC yr⁻¹. For year 2012 alone, E_FF grew to 9.7 ± 0.5 GtC yr⁻¹, 2.2% above 2011, reflecting a continued growing trend in these emissions, G_ATM was 5.1 ± 0.2 GtC yr⁻¹, S_OCEAN was 2.9 ± 0.5 GtC yr⁻¹, and assuming an E_LUC of 1.0 ± 0.5 GtC yr⁻¹ (based on the 2001–2010 average), S_LAND was 2.7 ± 0.9 GtC yr⁻¹. G_ATM was high in 2012 compared to the 2003–2012 average, almost entirely reflecting the high E_FF. The global atmospheric CO₂ concentration reached 392.52 ± 0.10 ppm averaged over 2012. We estimate that E_FF will increase by 2.1% (1.1–3.1%) to 9.9 ± 0.5 GtC in 2013, 61% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the economy. With this projection, cumulative emissions of CO₂ will reach about 535 ± 55 GtC for 1870–2013, about 70% from E_FF (390 ± 20 GtC) and 30% from E_LUC (145 ± 50 GtC). This paper also documents any changes in the methods and data sets used in this new carbon budget from previous budgets (Le Quéré et al., 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2013_V2.3).

Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these individual data sets over the time periods 1989–1995 (7 yr) and 1989–2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the individual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13 mm yr⁻² in our merged product) followed by a significant decrease in this trend (−0.18 mm yr⁻²), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr⁻¹ (1.35 mm d⁻¹) for both the 1989–1995 and 1989–2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr⁻¹ (34 406 km³ yr⁻¹) for a total land area of 130 922 000 km². Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.