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Long‐standing simulation errors limit the utility of climate models. Overlooked are tropical‐wide errors, with sea surface temperature (SST) biasing high or low across all the tropical ocean basins. Our analysis based on Coupled Model Intercomparison Project (CMIP) multi‐model ensembles shows that such SST biases can be classified into two types: one with a broad meridional structure and of the same sign across all basins that is highly correlated with the tropical mean; and one with large inter‐model variability in the cold tongues of the equatorial Pacific and Atlantic. The first type can be traced back to biases in atmospheric simulations of cloud cover, with cloudy models biasing low in tropical‐wide SST. The second type originates from the diversity among models in representing the thermocline depth; models with a deep thermocline feature a warm cold tongue on the equator. Implications for inter‐model variability in precipitation climatology and SST threshold for convection are discussed. Key Points Our analysis suggests two types of tropical‐wide SST biases in climate models The first type originates from biases in atmospheric simulations of cloud cover The second type is linked to oceanic representation of the thermocline depth

California has experienced devastating autumn wildfires in recent years. These autumn wildfires have coincided with extreme fire weather conditions during periods of strong offshore winds coincident with unusually dry vegetation enabled by anomalously warm conditions and late onset of autumn precipitation. In this study, we quantify observed changes in the occurrence and magnitude of meteorological factors that enable extreme autumn wildfires in California, and use climate model simulations to ascertain whether these changes are attributable to human-caused climate change. We show that state-wide increases in autumn temperature (∼1 °C) and decreases in autumn precipitation (∼30%) over the past four decades have contributed to increases in aggregate fire weather indices (+20%). As a result, the observed frequency of autumn days with extreme (95th percentile) fire weather-which we show are preferentially associated with extreme autumn wildfires-has more than doubled in California since the early 1980s. We further find an increase in the climate model-estimated probability of these extreme autumn conditions since ∼1950, including a long-term trend toward increased same-season co-occurrence of extreme fire weather conditions in northern and southern California. Our climate model analyses suggest that continued climate change will further amplify the number of days with extreme fire weather by the end of this century, though a pathway consistent with the UN Paris commitments would substantially curb that increase. Given the acute societal impacts of extreme autumn wildfires in recent years, our findings have critical relevance for ongoing efforts to manage wildfire risks in California and other regions.

This paper evaluates sea surface temperature (SST) biases of coupled models participating in Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. Overall, CMIP6 models perform better than CMIP5 ones in reproducing SST climatology, with lower multi‐model ensemble mean (MME) globally averaged absolute bias (1.17 vs. 1.31 K). MME bias in global mean annual SST shifts from cooling (−0.09 ± 0.52 K) to warming (0.23 ± 0.60 K). Regionally, in CMIP6 cooling biases over the Northwest Pacific and North Atlantic are reduced by 20% and 18%, while warming biases over the Northeast Pacific, Southeast Atlantic and Southern Ocean are increased by 25%, 16% and 107% respectively. These changes are mainly attributed to the combined effects from aggravated positive (or alleviated negative) bias in clear‐sky surface downward longwave radiation, and alleviated negative bias in cloud radiative effect, partially reduced by enhanced cooling bias in clear‐sky surface downward shortwave radiation. Plain Language Summary As the primary approach to projecting future climate change, state‐of‐the‐art climate models still suffer pronounced biases in climatological annual mean sea surface temperature (SST), such as cold biases over the Northwest Pacific and North Atlantic, and warm biases over the Northeast Pacific, Southeast Pacific, Southeast Atlantic and Southern Ocean. We have evaluated the changes in mean‐state SST biases between the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. CMIP6 models perform better in reproducing SST climatology with lower absolute bias, which is attributed to the process‐level improvement. Overall, annual global mean SST bias shifts from cold (−0.09 ± 0.52 K) to warm (0.23 ± 0.60 K), which is mainly due to the regionally alleviated cooling biases or aggravated warming biases. This warmer shift is contributed by the increased positive (or decreased negative) bias in clear‐sky surface downward longwave radiation and decreased negative bias in cloud radiative effect. Key Points Coupled Model Intercomparison Project Phase 6 (CMIP6) models perform better than CMIP5 ones, with significantly lower global‐mean absolute bias in annual sea surface temperature (SST) Global‐mean SST bias is with a warmer shift (+0.32 K) in CMIP6, with salient regional cold biases alleviated and warm biases aggravated Reduced cold bias in cloud radiative effect and positive change in bias in clear‐sky surface downward longwave together account for the shift

Concurrent extreme events, i.e. multi-variate extremes, can be associated with strong impacts. Hence, an understanding of how such events are changing in a warming climate is helpful to avoid some associated climate change impacts and better prepare for them. In this article, we analyse the projected occurrence of hot, dry, and wet extreme events' clusters in the multi-model ensemble of the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Changes in 'extreme extremes', i.e. events with only 1% probability of occurrence in the current climate are analysed, first as univariate extremes, and then when co-occurring with other types of extremes (i.e. events clusters) within the same week, month or year. The projections are analysed for present-day climate (+1 °C) and different levels of additional global warming (+1.5 °C, +2 °C, +3 °C). The results reveal substantial risk of occurrence of extreme events' clusters of different types across the globe at higher global warming levels. Hotspot regions for hot and dry clusters are mainly found in Brazil, i.e. in the Northeast and the Amazon rain forest, the Mediterranean region, and Southern Africa. Hotspot regions for wet and hot clusters are found in tropical Africa but also in the Sahel region, Indonesia, and in mountainous regions such as the Andes and the Himalaya.

This study provides a long-term (1891–2014) global assessment of precipitation trends using data from two station-based gridded datasets and climate model outputs evolved through the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). Our analysis employs a variety of modeling groups that incorporate low- and high-top level members, with the aim of assessing the possible effects of including a well-resolved stratosphere on the model’s ability to reproduce long-term observed annual precipitation trends. Results demonstrate that only a few regions show statistically significant differences in precipitation trends between observations and models. Nevertheless, this pattern is mostly caused by the strong interannual variability of precipitation in most of the world regions. Thus, statistically significant model-observation differences on trends (1891–2014) are found at the zonal mean scale. The different model groups clearly fail to reproduce the spatial patterns of annual precipitation trends and the regions where stronger increases or decreases are recorded. This study also stresses that there are no significant differences between low- and high-top models in capturing observed precipitation trends, indicating that having a well-resolved stratosphere has a low impact on the accuracy of precipitation projections.

Changes to the El Niño/Southern Oscillation (ENSO) and its atmospheric teleconnections under climate change are investigated using simulations conducted for the Coupled Model Intercomparison Project (CMIP5). The overall response to CO2increases is determined using 27 models, and the ENSO amplitude change based on the multi‐model mean is indistinguishable from zero. However, changes between ensembles run with a given model are sometimes significant: for four of the eleven models having ensemble sizes larger than three, the 21st century change to ENSO amplitude is statistically significant. In these four models, changes to SST and wind stress do not differ substantially from those in the models with no ENSO response, indicating that mean changes are not predictive of the ENSO sensitivity to climate change. Also, ocean vertical stratification is less (more) sensitive to CO2in models where ENSO strengthens (weakens), likely due to a regulation of the subsurface temperature structure by ENSO‐related poleward heat transport. Atmospheric teleconnections also show differences between models where ENSO amplitude does and does not respond to climate change; in the former case El Niño/La Niña‐related sea level pressure anomalies strengthen with CO2, and in the latter they weaken and shift polewards and eastwards. These results illustrate the need for large ensembles to isolate significant ENSO climate change responses, and for future work on diagnosing the dynamical causes of inter‐model teleconnection differences. Key Points ENSO amplitude is insignificant in the majority of IPCC‐class models ENSO amplitude change is not due to mean state or seasonal cycle changes The teleconnection response is sensitive to the ENSO amplitude change

Seasonal drought occurrences are found to increase across different regions over China under global warming, but with large uncertainties among models. With ten selected Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models and seven CMIP6 models according to their performances in reproducing historical drought trends ( p < 0.1), here we show that future seasonal soil moisture (SM) droughts over China projected by CMIP6 models are less frequent than that by CMIP5 models. We find national mean seasonal drought frequency is projected to increase by 28 ± 4% based on CMIP5 models at 1.5 °C global warming level, but only increase by 18 ± 6% based on CMIP6 models and 12 ± 4% based on land surface model ensemble simulations driven by downscaled CMIP5 models. Compared with CMIP6, CMIP5 projection suggests larger increase in precipitation but also larger increase in evapotranspiration, leading to more frequent seasonal SM droughts. Comparing the results at 3 °C global warming level with those at 1.5 °C, drought frequency over China will increase further by 10 ± 4%, but drought duration will decrease by 6 ± 4%, suggesting more frequent seasonal SM droughts with shorter durations will occur in a warming future. The future increase in China drought frequency will reduce from 12%–45% based on selected climate models to 3%–27% based on all available models (30 CMIP5 models and 31 CMIP6 models), which indicates that the model selection is critical for future drought projection. Nevertheless, CMIP6 still projects less frequent seasonal SM droughts than CMIP5 even without any model discriminations.

Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

Contemporary general circulation models (GCMs) and Earth system models (ESMs) are developed by a large number of modeling groups globally. They use a wide range of representations of physical processes, allowing for structural (code) uncertainty to be partially quantified with multi‐model ensembles (MMEs). Many models in the MMEs of the Coupled Model Intercomparison Project (CMIP) have a common development history due to sharing of code and schemes. This makes their projections statistically dependent and introduces biases in MME statistics. Previous research has focused on model output and code dependence, and model code genealogy of CMIP models has not been fully analyzed. We present a full reconstruction of CMIP3, CMIP5, and CMIP6 code genealogy of 167 atmospheric models, GCMs, and ESMs (of which 114 participated in CMIP) based on the available literature, with a focus on the atmospheric component and atmospheric physics. We identify 12 main model families. We propose family and ancestry weighting methods designed to reduce the effect of model structural dependence in MMEs. We analyze weighted effective climate sensitivity (ECS), climate feedbacks, forcing, and global mean near‐surface air temperature, and how they differ by model family. Models in the same family often have similar climate properties. We show that weighting can partially reconcile differences in ECS and cloud feedbacks between CMIP5 and CMIP6. The results can help in understanding structural dependence between CMIP models, and the proposed ancestry and family weighting methods can be used in MME assessments to ameliorate model structural sampling biases. Plain Language Summary Contemporary global climate models are developed by a large number of modeling groups internationally. Commonly, projections from multiple models are used together to calculate multi‐model means and quantify uncertainty. Because many of the models share parts of their computer code, algorithms and parametrization schemes, they are not independent. Overrepresented models can cause biases in multi‐model means, and uncertainty may be underestimated if model dependence is not taken into account. We document a full code genealogy of 167 models, of which 114 participated in the Coupled Model Intercomparison Project (CMIP) phases 3, 5, and 6, with a focus on the atmospheric component. We identify 12 main model families. We show that models in the same family often have similar estimates of key climate properties. We propose statistical weighting methods based on the model family and code relationship, and show that they can reconcile some of the difference in results between the two most recent CMIP phases. The weighting methods or a selection of independent models based on the genealogy can be used in model assessment studies to reduce the effects of model dependence. Key Points We reconstruct a code genealogy of 167 climate models with a focus on the atmospheric component and atmospheric physics All models originate from 12 main model families, and models in the same family often have similar climate feedbacks and sensitivity Proposed ancestry and family weighting can partly reconcile differences in means between the Coupled Model Intercomparison Project phases

The new emerging type of El Niño brings challenges to the state-of-the-art coupled models to simulated features of El Niño - Southern Oscillation (ENSO) diversity. The Coupled Model Intercomparison Project (CMIP), containing the advanced worldwide coupled models, has recently released the model outputs in phase 6. In this paper, the characteristics of two types of ENSO in 19 models from CMIP phase 5 and their counterparts in phase 6 are assessed regarding the spatial and temporal features and the seasonal cycle features. The weaker amplitude of Eastern Pacific (EP) and Central Pacific (CP) ENSO in CMIP5 is increased and the spatial structure of CP ENSO is better depicted in CMIP6. However, no significant improvement in the ENSO periodicity and the ENSO phase-locking behavior compared to CMIP5. A synthetic ENSO score, containing eight metrics, is defined and employed to evaluate the performance of each CMIP model. The average ENSO score for CMIP6 is 2.375, indicating modest improvement compared to the average score of 2.441 for CMIP5. Furthermore, the slight improvement in the ENSO score is partly related to the reduced climatology bias of sea surface temperature in the Niño4 region. The overall evaluation provides necessary information for future investigation about the mechanism exploration of the ENSO diversity based on the models with better performance.