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We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.

This paper compares the historical simulations and future projections of surface air temperature over the Tibetan Plateau of the updated Coupled Model Intercomparison Project phase (CMIP6) and the precedent phase of the project (CMIP5) to quantify differences in the projections under different scenarios. Model evaluation for the historical period (1961–2005) indicates that the multi-model ensemble (MME) mean of CMIP6 outperforms CMIP5 MME in simulating spatial–temporal characteristics of surface air temperature. The temperature changes relative to 1986–2005 are projected in the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) future under Shared Socio-economic Pathway (SSP)2-4.5 and SSP5-8.5 of CMIP6 and Representative Concentration Pathway (RCP)4.5 and RCP8.5 of CMIP5. The projected temperature shows larger increases in the long-term projection compared with the near- and mid-term projection under both SSPs and RCPs. CMIP6 MME projects higher temperature changes and accelerated warming trends relative to CMIP5 MME. Additionally, the projected temperature increases and warming rates show a significant elevation dependency, especially in the long-term projection. The uncertainty for future projections is quantified by the square root of error variance (SREV) method. The results record a clear reduction in the uncertainty of CMIP6 temperature relative to CMIP5 primarily concentrated at the elevation zones of over 5000 m. The analysis of the projected temperature over the Tibetan Plateau is of great significance for policy-makers to make socio-economic adjustments for the future warming. This study is conducive to the credibility of future temperature projections for CMIP6 and enhances our comprehension of the uncertainty of SSP and RCP scenarios.

Past climates provide a test of models’ ability to predict climate change. We present a comprehensive evaluation of state-of-the-art models against Last Glacial Maximum and mid-Holocene climates, using reconstructions of land and ocean climates and simulations from the Palaeoclimate Modelling and Coupled Modelling Intercomparison Projects. Newer models do not perform better than earlier versions despite higher resolution and complexity. Differences in climate sensitivity only weakly account for differences in model performance. In the glacial, models consistently underestimate land cooling (especially in winter) and overestimate ocean surface cooling (especially in the tropics). In the mid-Holocene, models generally underestimate the precipitation increase in the northern monsoon regions, and overestimate summer warming in central Eurasia. Models generally capture large-scale gradients of climate change but have more limited ability to reproduce spatial patterns. Despite these common biases, some models perform better than others.

We evaluate extratropical modes of variability in the three most recent phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) to gauge improvement of climate models over time. A suite of high-level metrics is employed to objectively evaluate how well climate models simulate the observed northern annular mode (NAM), North Atlantic Oscillation (NAO), Pacific–North America pattern (PNA), southern annular mode (SAM), Pacific decadal oscillation (PDO), North Pacific Oscillation (NPO), and North Pacific Gyre Oscillation (NPGO). We apply a common basis function (CBF) approach that projects model anomalies onto observed empirical orthogonal functions (EOFs), together with the traditional EOF approach, to CMIP Historical and AMIP models. We find simulated spatial patterns of those modes have been significantly improved in the newer models, although the skill improvement is sensitive to the mode and season considered. We identify some potential contributions to the pattern improvement of certain modes (e.g., the Southern Hemisphere jet and high-top vertical coordinate); however, the performance changes are likely attributed to gradual improvement of the base climate and multiple relevant processes. Less performance improvement is evident in the mode amplitude of these modes and systematic overestimation of the mode amplitude in spring remains in the newer climate models. We find that the postdominant season amplitude errors in atmospheric modes are not limited to coupled runs but are often already evident in AMIP simulations. This suggests that rectifying the egregious postdominant season amplitude errors found in many models can be addressed in an atmospheric-only framework, making it more tractable to address in the model development process.

The topography of Madagascar and the strength of the Mozambique Channel Trough (MCT) modulate summer rainfall over southern Africa. A strong MCT hinders the penetration of moisture bearing easterlies from the South Indian Ocean into the mainland, thus reducing rainfall there and vice versa for weak MCT summers. Given the link between the MCT and rainfall, it is important to analyse how climate models represent the trough. Here, output from 20 models within the CMIP6 ensemble of Coupled General Circulation Models (CGCMs) are analyzed to investigate how state-of-the-art CGCMs represent the MCT and its link to southern African rainfall. Overall, the ensemble mean insignificantly underestimates the observed MCT. There is a large spread among the models, with the strength of the MCT significantly correlated with the Froude number based on the mountain height over Madagascar. In models, the vorticity tendency in the MCT area is dominated by the stretching and friction terms, whereas the vertical advection, tilting and residual terms dominate in the ERA5 reanalysis. The link between MCT and rainfall in the southern African subcontinent is missing in the models. Large rainfall biases are depicted over mainland even in models with a very strong MCT. It is found that the impacts of the MCT in the models could be masked by a complex mix of processes such as the strength of the Angola low, moisture fluxes from the Indian and South Atlantic Oceans as well as overestimated convection in the Mozambique Channel area.

Tree‐ring width (RW) records primarily capture low‐frequency temperature variability, yet resolving high‐frequency signals is critical for testing climate models and contextualizing modern extremes. Blue rings (BRs)—bands of unlignified cells revealed by micro‐anatomical staining—capture short‐lived cooling events. Using 83 Pinus longaeva cores, we present the first millennial‐length (900–2014 CE) BR chronology and explore its paleoclimatic significance. BRs are largely decoupled from growth reductions: they often occur in rings of normal or above‐average width, preceding RW minima by one year, recording abrupt late‐season cooling that does not immediately suppress growth but frequently triggers reduced growth the following year. Events include anomalies associated with major volcanic eruptions, whose impacts are delayed and smoothed in RW chronologies. By capturing transient temperature declines with annual precision, BRs provide a novel dendroclimatic proxy that bridges low‐frequency and sub‐seasonal climate signals, offering benchmarks for climate‐model evaluation and attribution under past and contemporary climate change.

Quantitative simulation of precipitation in current climate has been an ongoing challenge for global climate models. Despite serious biases in correctly simulating probabilities of extreme rainfall events, model simulations under global warming scenarios are routinely used to provide estimates of future changes in these probabilities. To minimize the impact of model biases, past literature tends to evaluate fractional (instead of absolute) changes in probabilities of precipitation extremes under the assumption that fractional changes would be more reliable. However, formal tests for the validity of this assumption have been lacking. Here we evaluate two measures that address properties important to the correct simulation of future fractional probability changes of precipitation extremes, and that can be assessed with current climate data. The first measure tests climate model performance in simulating the characteristic shape of the probability of occurrence of daily precipitation extremes and the second measure tests whether the key parameter governing the scaling of this shape is well reproduced across regions and seasons in current climate. Contrary to concerns regarding the reliability of global models for extreme precipitation assessment, our results show most models lying within the current range of observational uncertainty in these measures. Thus, most models in the Coupled Model Intercomparison Project Phase 6 ensemble pass two key tests in current climate that support the usefulness of fractional measures to evaluate future changes in the probability of precipitation extremes.

Subfossil pollen and plant macrofossil data derived from 14 C-dated sediment profiles can provide quantitative information on glacial and interglacial climates. The data allow climate variables related to growing-season warmth, winter cold, and plant-available moisture to be reconstructed. Continental-scale reconstructions have been made for the mid-Holocene (MH, around 6 ka) and Last Glacial Maximum (LGM, around 21 ka), allowing comparison with palaeoclimate simulations currently being carried out as part of the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change. The synthesis of the available MH and LGM climate reconstructions and their uncertainties, obtained using modern-analogue, regression and model-inversion techniques, is presented for four temperature variables and two moisture variables. Reconstructions of the same variables based on surface-pollen assemblages are shown to be accurate and unbiased. Reconstructed LGM and MH climate anomaly patterns are coherent, consistent between variables, and robust with respect to the choice of technique. They support a conceptual model of the controls of Late Quaternary climate change whereby the first-order effects of orbital variations and greenhouse forcing on the seasonal cycle of temperature are predictably modified by responses of the atmospheric circulation and surface energy balance.

The Congo Basin is one of three key areas of tropical convection and contains the planet’s second largest rainforest. Understanding how global warming might change its climate is crucial, particularly during the dry seasons, when rainfall amounts currently bring the rainforest boundaries close to the threshold of viability. There is considerable uncertainty in projections of future rainfall change from the Coupled Model Intercomparison Project (CMIP5) under the high-emissions experiment (RCP8.5). Whilst there is a general trend towards wetting in most months, its magnitude varies considerably. In the December to February dry season, the projected change in seasonal rainfall varies from 2 to 160 mm across models. This study uses a regionally-focused process-based assessment to understand inter-model differences in rainfall projections, as a first step to assessing their plausibility. Models which produce the most wetting by the end of the century feature enhanced convection over the Congo Basin region, enhanced subsidence in the African subtropics, and decreased uplift over the Maritime Continent. In contrast, models with a small wetting response feature reduced convection over the Congo Basin. This indicates that wetting over the Congo Basin is related to a weakening of the Indian Ocean Walker circulation, reminiscent of a positive Indian Ocean Dipole state. Models with the highest magnitude wetting also feature greater low-to-mid-level moisture flux from the north and the east compared to models with less wetting. These results indicate that the future degree of wetting over the Congo Basin will be linked to changes in convection over the Maritime Continent.

This work provides a significant contribution on the open debate in the climate community to establish the added value of very high-resolution configurations, characterized by a horizontal resolution below 4 km with respect to current state-of-the-art climate simulations (10–15 km). Specifically, it aims at assessing quantitative gains and losses in the performance of climate models caused by an enhancement in temporal and spatial resolution by evaluating the capability of different climate simulations in reproducing daily and sub-daily present precipitation dynamics over a complex orographic context such as the Alpine region. In this perspective, the results of three experiments (EURO-CORDEX ensemble mean, CCLM 8 and CCLM 2.2) at different spatial (~ 12, 8 and 2.2 km) and temporal (daily, 6 h and 3 h) scales are compared to gridded and point-scale observational datasets. Precipitation data are analyzed by mean of the Expert Team on Climate Change Detection and Indices indicators, as well as with statistical models able to evaluate the precipitation distribution and the extreme values for different durations of precipitation events. To objectively assess gains and losses in adopting high-resolution RCMs, data are elaborated assuming the distribution added value as metric, particularly focusing on the role of orography. The work returns, at daily scale, a gain in climate model performances moving from lower to higher horizontal resolution. At the same time, investigating the effect of the orography the simulation with the finest grid proves to better capture local precipitation dynamics at higher altitudes in terms of both sub-daily precipitation and extreme events.