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Forcing due to solar and volcanic variability, on the natural side, and greenhouse gas and aerosol emissions, on the anthropogenic side, are the main inputs to climate models. Reliable climate model simulations of past and future climate change depend crucially upon them. Here we analyze large ensembles of simulations using a comprehensive Earth System Model to quantify uncertainties in global climate change attributable to differences in prescribed forcings. The different forcings considered here are those used in the two most recent phases of the Coupled Model Intercomparison Project (CMIP), namely CMIP5 and CMIP6.We show significant differences in simulated global surface air temperature due to volcanic aerosol forcing in the second half of the 19th century and in the early 21st century. The latter arise from small-to-moderate eruptions incorporated in CMIP6 simulations but not in CMIP5 simulations. We also find significant differences in global surface air temperature and Arctic sea ice area due to anthropogenic aerosol forcing in the second half of the 20th century and early 21st century. These differences are as large as those obtained in different versions of an Earth System Model employing identical forcings. In simulations from 2015 to 2100, we find significant differences in the rates of projected global warming arising from CMIP5 and CMIP6 concentration pathways that differ slightly but are equivalent in terms of their nominal radiative forcing levels in 2100. Our results highlight the influence of assumptions about natural and anthropogenic aerosol loadings on carbon budgets, the likelihood of meeting Paris targets, and the equivalence of future forcing scenarios.

The upper‐level Hadley circulation is critical to the energy transport from tropical toward mid‐to‐high latitude regions. In this study, an index denoting the intensity of upper‐level Hadley circulation in Northern Hemisphere (Southern Hemisphere) is defined as the areal mean zonal mean meridional mass stream function in the upper‐level Hadley circulation, which is referred to as UP‐NH‐HCI (UP‐SH‐HCI). An increasing (decreasing) trend of UP‐NH‐HCI (UP‐SH‐HCI) is observed during 1979‐present using five reanalysis data sets, which denotes increased intensity of upper‐level Hadley circulation in Northern Hemisphere (Southern Hemisphere). Furthermore, Nine (13) CMIP6 models are selected to perform the detection and attribution analysis for the trends in UP‐NH‐HCI (UP‐SH‐HCI). Using the optimal fingerprinting method, the results indicate that the greenhouse gas forcing accounts for 57%–114% and 61%–89% of the observed changes in UP‐NH‐HCI and UP‐SH‐HCI, respectively, at an uncertainty range of 10%–90%.

In this study, the influences of different PDO phases during strong and weak solar radiation periods on precipitation over eastern China during the last millennium were studied by using four solar irradiance only sensitivity experiments (SSI) and one control experiment (CTRL) from Community Earth System Model - Last Millennium Ensemble (CESM-LME) archive. Results show that there are significant positive (negative) precipitation anomalies over eastern China during the strong (weak) solar radiation periods. However, the precipitation differences between positive PDO and negative PDO phases in the SSI experiments are smaller compared with CTRL. Moreover, there is a dependency relationship between the solar radiation intensities and precipitation differences between positive PDO and negative PDO phases. The mechanism analyses show that southerly wind anomalies continuously increase with solar radiation intensities during the positive PDO phases and result in decreased precipitation anomalies, vice versa. During the positive PDO phases, the continental sea level pressure (SLP) gradient is the dominant factor of the wind anomalies over eastern China. The continental SLP gradient over the mainland of China (north-south) is strengthened with the solar radiation intensities, resulting in the decrease of precipitation over eastern China. While, during the negative PDO phases, the sea-land SLP gradient is the main factor of the wind anomalies. The sea-land SLP gradients decreas with the solar radiation intensities, so the southerly wind and the precipitation over eastern China also increase.

Multidecadal sea surface temperature (SST) variations in the tropical western Pacific (TWP) have been attributed to nonlinear external forcing and remote influences from the Atlantic Multidecadal Variability (AMV). However, the AMV resulted from both internal variability (IV) and external forcing. Thus, the origins of the TWP SST variations are not well understood. By analyzing observations and model simulations, we show that more than half of the decadal to multidecadal SST variations in TWP during 1920–2020 resulted from external forcing with the forced component correlated with AMV, while the internal component is unrelated to AMV. Furthermore, about 43%–49% of the forced AMV‐like SST variations in TWP result from remote influences of the forced AMV in the Atlantic via atmospheric teleconnection over the North Pacific, with the rest from other remote or local processes. Plain Language Summary Sea surface temperature (SST) variations in the North Atlantic Ocean and Pacific Ocean are linked on multidecadal time scales. Previous studies have indicated that the Atlantic multidecadal SST variations (referred to as Atlantic multidecadal variability, AMV) not only influence the decadal SST variations in the tropical central‐eastern Pacific (referred to as Interdecadal Pacific Oscillation), but also modulate the multidecadal SST variations in the tropical western Pacific (TWP). Since the TWP SST variations can result from both local external forcing and remote influences from AMV, the exact origins of TWP's multidecadal variability remains unclear. Here we analyze observations and model experiments to show that ∼56% of the decadal to multidecadal SST variations in TWP during 1920–2020 resulted from nonlinear external forcing, with the rest from internal variability (IV), and the forced SST variations in TWP are correlated with AMV while the IV‐induced variations are unrelated to AMV. Both the local external forcing and remote influences from the forced AMV in the Atlantic contribute to the forced multidecadal SST variations in TWP. Key Points Atlantic Multidecadal Variability (AMV) is mainly correlated with the forced decadal sea surface temperature (SST) variations in tropical western Pacific (TWP) during 1920–2020 About 43%–49% of forced TWP SST variations, which account for more than half of the total decadal variations, come from the Atlantic Ocean The connections between the externally‐forced AMV and TWP SST are through the extratropical pathway in the North Pacific

Rainfall over mainland Southeast Asia experiences variability on seasonal to decadal timescales in response to a multitude of climate phenomena. Historical records and paleoclimate archives that span the last millennium reveal extreme multi-year rainfall variations that significantly affected the societies of mainland Southeast Asia. Here we utilize the Community Earth System Model Last Millennium Ensemble (CESM-LME) to quantify the contributions of internal and external drivers to decadal-scale rainfall extremes in the Southeast Asia region. We find that internal variability was dominant in driving both Southeast Asian drought and pluvial extremes on decadal timescales although external forcing impacts are also detectable. Specifically, rainfall extremes are more sensitive to Pacific Ocean internal variability than the state of the Indian Ocean. This discrepancy is greater for droughts than pluvials which we suggest is attributable to external forcing impacts that counteract the forced Indian Ocean teleconnections to Southeast Asia. Volcanic aerosols, the most effective radiative forcing during the last millennium, contributed to both the Ming Dynasty Drought (1637–1643) and the Strange Parallels Drought (1756–1768). From the Medieval Climate Anomaly to the Little Ice Age, we observe a shift in Indo-Pacific teleconnection strength to Southeast Asia consistent with enhanced volcanism during the latter interval. This work not only highlights asymmetries in the drivers of rainfall extremes but also presents a framework for quantifying multivariate drivers of decadal-scale variability and hydroclimatic extremes.

In December 2022, North America experienced an unprecedented extreme cold event. However, the underlying physical mechanisms of this cold wave, and the extent to which it is driven by internal variability or external forcing, are not fully understood. Using ERA5 reanalysis data and the HadGEM3‐A‐N216 attribution simulations, we identified internal variability as the main cause, contributing −5.14 K to surface air temperature (SAT) anomalies in North America. External forcing slightly mitigated the cold by 0.42 K. An internally generated wave train from the North Pacific, influenced in combination by Pacific‐North American (PNA) and North Pacific Oscillation (NPO) teleconnection patterns, initiated this intense cyclonic event, contributing −2.18 K and −2.12 K to SAT anomalies, respectively. La Niña‐like sea surface temperature anomalies amplified this wave train and resultant cold wave. Additionally, excessive snow cover in the previous November also intensified the December cold anomalies by enhancing surface albedo and reducing solar radiation. Plain Language Summary In December 2022, North America was hit by an exceptionally severe cold event. Scientists have been trying to understand the reasons behind this cold wave. Using detailed weather data and climate models, researchers found that natural climate variability played a major role, causing temperatures to drop by about 5.14 degrees Celsius. On the other hand, external factors like human‐induced climate change had a minor effect, slightly reducing the severity of the cold by 0.42 degrees Celsius. The study identified an atmospheric wave train pattern, originating from the North Pacific and moving toward North America, which played a crucial role in triggering this extreme weather. Further investigations showed that this wave train was influenced by specific large‐scale weather patterns in the Pacific region, namely the Pacific‐North American (PNA) and North Pacific Oscillation (NPO) patterns, which contributed to the cold temperatures by approximately 2.18 and 2.12 degrees Celsius, respectively. Additionally, colder‐than‐normal sea surface temperatures in the tropical central Pacific, associated with La Niña, strengthened the wave train. Internal thermodynamical processes, such as increased snow cover, also slightly intensified the cold wave by reflecting more sunlight and reducing the amount of solar energy reaching the surface. Key Points Internal variability triggers 2022 December extreme cold wave in North America PNA and NPO are two key teleconnections responsible for this extreme cold wave Snow cover‐SAT feedback intensifies this extreme cold wave

Summers have become hotter in recent decades, with earlier onsets in many regions. Here, we explore changes in the summer length under global warming based on the observations and CMIP6 models, identify the influences of external forcing and internal variability, and use CMIP6 models to project variations of the future summer length. Summer is defined as when the daily mean temperature is above the 1961–1990 75th percentile for 5 consecutive days. The summer length significantly increases, and the observed trends show marked regional differences. External forcing is the main contributor to the lengthening of summer, while internal variability may explain the regional differences. Our results reveal that a 1 ℃ global surface mean temperature increase is associated with 15 days of the summer length increase during 1961–2014 in the observations, while a 1 ℃ temperature increase corresponds to 10 days of the summer length increase in CMIP6 models. CMIP6 models are also used to project the change of the summer length in the future, and it is found that the summer length will continue to increase in the future. Summer will last 142 days (175 days) under SSP2-4.5 (SSP5-8.5) scenario of global warming by the end of the twenty-first century, equivalent to an approximate 1.2 (1.5)-fold increase relative to that of 2014.

Anthropogenic aerosols (AA) induce pronounced East Asian summer monsoon (EASM) changes since the industrial revolution. However, the regional contribution from different AA emission sources is hard to quantify due to AA’s heterogeneous spatial distribution and the nonmonotonic trend at decadal time scale. Using coupled climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) and Community Earth System Model 1 (CESM1) large ensemble simulations, we investigate EASM responses between 1950–1980 and 1980–2010, to understand how the remote influence of changes in the AA emissions from Europe modulates the EASM at decadal time scale. AA emissions from Europe increased early in the latter half of the twentieth century and then decreased rapidly after the 1980s. During 1950–1980, the increase of AA emissions from Europe, together with the localized increase of AA emissions from East Asia, weakens the EASM by generating the tropospheric cooling and shifting the East Asian subtropical jet equatorward. However, during 1980–2010, the declined AA emissions from Europe generate the tropospheric warming and induce an atmospheric teleconnection pattern that initiate at the heating anomaly and propagate downstream to northeast Asia following the westerly jet, leading to an enhanced EASM. This enhancement due to the remote influence of declined AA emissions from Europe explains why after the 1980s, despite the localized increase of AA emissions from East Asia, coupled climate models results show that the EASM is intensified by the anomalous southerlies and the precipitation increase in Northeast Asia. Our results imply that at the long-term change view, the local AA emissions dominate the EASM response, while the non-local European AA emissions play a more important role in shaping the decadal EASM changes.

Global mean surface temperature (GMST) rising has slowed down since late 1990s, which is referred to as the global warming hiatus. There was another global warming hiatus event during 1940s–1960s. The roles of the external forcing and the natural variability in both global warming hiatuses are explored, using EOF analysis. The first two leading EOF modes of the 5-year running mean global sea surface temperature (SST) reflect the global warming scenario (EOF1) and the interdecadal Pacific oscillation (IPO)-like natural variability (EOF2), respectively. In observation, PC2 was in its positive phase (eastern Pacific cooling) during 1940s–1960s, which contributed to the previous warming hiatus. In addition, GMST trends are found to be negative during late 1950s and 1960s in most of the CMIP5 historical runs, which implies that the external forcing also contributed to the pause in the GMST rising. It is further demonstrated that it is the natural radiative forcing (volcanic forcing) that caused the drop-down of GMST in 1960s. The current global warming hiatus has been attributed to the eastern Pacific cooling/enhanced Pacific trade winds. It is shown that the PC2 switched to its positive phase in late 1990s, and hence the IPO-like natural variability made a contribution to the slowdown of GMST rising in the past decade. It is also found that the EOF1 mode (global warming mode) of the observed SST features a smaller warming in tropical Pacific compared to the Indian Ocean and the tropical Atlantic. Such inter-basin warming contrast, which is attributed to the “ocean thermostat” mechanism, has been suggested to contribute to the intensification of Pacific trade winds since late 1990s as well. Global warming hiatuses are also found in the future projections from CMIP5 models, and the spatial pattern of the SST trends during the warming-hiatus periods exhibits an IPO-like pattern, which resembles the observed SST trends since late 1990s.

Arctic warming has significant environmental and social impacts. Arctic long‐term warming trend is modulated by decadal‐to‐multidecadal variations. Improved understanding of how different external forcings and internal variability affect Arctic surface air temperature (SAT) is crucial for explaining and predicting Arctic climate changes. We analyze multiple observational data sets and large ensembles of climate model simulations to quantify the contributions of specific external forcings and various modes of internal variability to Arctic SAT changes during 1900–2021. We find that the long‐term trend and total variance in Arctic‐mean SAT since 1900 are largely forced responses, including warming due to greenhouse gases and natural forcings and cooling due to anthropogenic aerosols. In contrast, internal variability dominates the early 20th century Arctic warming and mid‐20th century Arctic cooling. Internal variability also explains ∼40% of the recent Arctic warming from 1979 to 2021. Unforced changes in Arctic SAT are largely attributed to two leading modes. The first is pan‐Arctic warming with stronger loading over the Eurasian sector, accounting for 70% of the unforced variance and closely related to the positive phase of the unforced Atlantic Multidecadal Oscillation (AMO). The second mode exhibits relatively weak warming averaged over the entire Arctic with warming over the North American‐Pacific sector and cooling over the Atlantic sector, explaining 10% of the unforced variance and likely caused by the positive phase of the unforced Interdecadal Pacific Oscillation (IPO). The AMO‐related changes dominate the unforced Arctic warming since 1979, while the IPO‐related changes contribute to the decadal SAT changes over the North American‐Pacific Arctic. Plain Language Summary The Arctic warms much faster than the rest of the world, leading to significant local and remote influences. Warming in the Arctic is not uniform over time, with decadal‐to‐multidecadal variations upon the long‐term trend. The changes in Arctic surface air temperature (SAT) can be attributed to either instrinct variability within the climate system or external forcings including anthropogenic factors such as greenhouse gases emission and natural factors such as volcanic eruptions. Understanding of the relative contributions of internal variability and external forcing to observed changes in Arctic SAT is crucial for improving Arctic climate projections in coming decades. By synthesizing multiple observational data sets and large‐ensemble climate simulations, we find that the Arctic experienced long‐term warming with some periods of slowdown in response to external forcing, which largely explains the overall change since 1900. Internal variability, particularly the multidecadal oscillation in the North Atlantic, dominates the early 20th century warming and mid‐20th century cooling and significantly contributes to the recent rapid warming since 1979. A regression‐based rescaling method removes systematic biases in model‐simulated response (in comparison with observations), ensuring that our results are not influenced by the choice of climate models, as long as they are under the same historical forcing. Key Points Externally forced Arctic warming dominates the trend and variance in Arctic surface air temperature during 1900–2021 Most of the internally generated Arctic temperature changes are related to the unforced Atlantic Multidecadal Oscillation Internal variability explains 40% of the Arctic warming from 1979 to 2021, while greenhouse gases and natural forcings account for the rest