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The capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi‐decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse‐resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream. Plain Language Summary: The Northwest Atlantic Shelf is experiencing a rapid rise in ocean temperatures, one of the fastest globally, impacting the region's marine resources. Global coupled models struggle to accurately simulate this regional warming and have large uncertainties associated with the future evolution of this warming. To address this issue, we developed a high‐resolution decadal prediction system for the Northwest Atlantic Shelf which downscales global decadal predictions using a high‐resolution regional ocean model. The downscaled simulations accurately predict past oceanic variability and forecast a temporary pause in warming over the next decade due to natural changes in the Atlantic meridional overturning circulation and the position of the Gulf Stream. This predictive capability could pave the way for the implementation of effective mitigation strategies, benefiting coastal communities and marine resources alike. Key Points: Dynamical downscaling enhances decadal prediction skill of Northwest Atlantic Shelf sea surface temperaturesA temporary respite from rapid Northwest Atlantic Shelf warming is forecast for the coming decadeThe forecast warming pause is attributed to a modest strengthening of the Atlantic meridional overturning circulation [ABSTRACT FROM AUTHOR]

Abstract The capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi‐decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse‐resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream.

Seasonal snowpack in the Western United States (WUS) is vital for meeting summer hydrological demands, reducing the intensity and frequency of wildfires, and supporting snow‐tourism economies. While the frequency and severity of snow droughts (SD), that is, anomalously low snowpacks, are expected to increase under continued global warming, the uncertainty from internal climate variability remains challenging to quantify with observations alone. Using a 30‐member large ensemble from a state‐of‐the‐art global climate model, the Seamless System for Prediction and EArth System Research (SPEAR), and an observations‐based data set, we find WUS SD changes are already significant. By 2100, SPEAR projects SDs to be nearly 9 times more frequent under shared socioeconomic pathway 5‐8.5 (SSP5‐8.5) and 5 times more frequent under SSP2‐4.5, compared to a 1921–2011 average. By investigating the influence of the two primary drivers of SD, temperature and precipitation amount, we find the average WUS SD will become warmer and wetter. To assess how these changes affect future summer water availability, we track late winter and spring snowpack across WUS watersheds, finding differences in the onset time of a "no‐snow" threshold between regions and large internal variability within the ensemble that are both on the order of decades. We attribute the inter‐regional variability to differences in the regions' mean winter temperature and the intra‐regional variability to irreducible internal climate variability which is not well‐explained by temperature variations alone. Despite strong scenario forcing, internal climate variability will continue to drive variations in SD and no‐snow conditions through 2100. Plain Language Summary: Snow drought (SD) occurs when there is significantly less snow on the ground than normal. SDs can intensify water shortages, accelerate wildfires, and harm snow‐based tourism economies. For the Western United States, whose water supply is already limited, a recent increase in snow drought frequency is particularly concerning. Here, we use observational data and a new climate model to examine snow drought changes across the region between 1921 and 2100. We find SDs are already more common and could increase almost nine times under a high emissions scenario or five times under moderate emissions cuts by 2100. To better understand the increase, we tracked the evolution of the two main snow drought drivers: warmer temperatures and decreased precipitation. We find the average snow drought will become warmer and wetter, indicating warming temperatures are driving the increase. As the model consists of multiple simulations of future climate, or ensemble members, that differ only in the realization of random climate variability, we can determine when Western regions are expected to lose most of their spring snowpack. We find that loss timing varies dramatically between regions and ensemble members, suggesting random climate variability will shape the West's future water availability. Key Points: Excluding the Pacific Northwest, observed increases of Western U.S. severe snow drought by 4%–46% are supported by the Seamless System for Prediction and EArth System Research (SPEAR) climate model21st century increases in Western U.S. severe snow drought in SPEAR are driven by rising temperatures and not decreased precipitationBy 2100 under SSP5‐8.5, SPEAR projects persistent Western U.S. spring no‐snow with timing sensitive to internal climate variability [ABSTRACT FROM AUTHOR]

Modeling tritium content in water presents a meaningful way to evaluate the representation of the water cycle in climate models as it traces fluxes within and between the reservoirs involved in the water cycle (stratosphere, troposphere, and ocean). In this study, we present the implementation of natural tritium in water in the atmospheric general circulation model (AGCM) MIROC5‐iso and its simulation for the period 1979–2018. Owing to recently published tritium production calculations, we were able to investigate, for the first time, the influence of natural tritium production related to the 11‐yr solar cycle on tritium in precipitation. MIROC5‐iso correctly simulates continental, latitudinal, and altitude effects on tritium in precipitation. The seasonal tritium content peaks, linked to stratosphere‐troposphere exchanges, are accurately simulated in terms of timing, even though MIROC5‐iso underestimates the amplitude of the changes. Decadal tritium concentration variations in precipitation owing to the 11‐yr solar cycle are well simulated in MIROC5‐iso, in agreement with the observations at Vostok in Antarctica for example, Finally, our simulations revealed that the internal climate variability plays an important role in tritium in polar precipitation. Owing to its influence on the south polar vortex, the Southern Annular Mode enhances the effect of the production component on tritium in East Antarctic precipitation. In Greenland, we found an east‐west contrast in the detection of the 11‐yr solar cycle in tritium in precipitation owing to the influence of the North Atlantic Oscillation on humidity conditions. Plain Language Summary: Models used to simulate the future of our climate are still struggling to represent the water cycle adequately. To address this challenge, we have added the capability to simulate tritium in water into the atmospheric model MIROC5‐iso. Tritium is a naturally occurring radioactive isotope produced by interactions of cosmic rays in the atmosphere. Because it decays away with a half‐life of 12.32 years, it is a very powerful tracer of water exchanges within and between water cycle reservoirs, such as the stratosphere, the troposphere, and the ocean. We were also able to investigate the influence of the 11‐year solar cycle on tritium in precipitation, as well as the impact of natural atmospheric circulation variations on an interannual time scale. MIROC5‐iso can simulate decadal variations in tritium in precipitation related to solar cycles. The presence or absence of this cycle in our modeled tritium for polar regions is influenced by interannual variations in the atmospheric circulation, especially related to precipitation amount variations. Conclusively, we show that tritium is reliable complementary tracer for better constraining the water cycle representation in climate models. Key Points: For the first time, natural tritium (HTO) is modeled in an atmospheric model by considering solar modulation over the period 1979–2018MIROC5‐iso correctly captures the observed spatial distribution of tritium in precipitation and its temporal variationsInternal climate modes can enhance or hide changes in tritium in precipitation owing to the decadal tritium production variations [ABSTRACT FROM AUTHOR]

Continued climate warming, together with the overall evaluation and implementation of a range of climate mitigation and adaptation approaches, has prompted increasing research into proposed solar climate intervention methods, such as stratospheric aerosol injection (SAI). SAI would use aerosols to reflect a small amount of incoming solar radiation away from Earth to stabilize or reduce future warming due to increasing greenhouse gas concentrations. Research into the possible risks and benefits of SAI relative to the risks from climate change is emerging. There is not yet, however, an adequate understanding of how SAI might impact human and natural systems. For instance, little to no research to date has examined how SAI might impact environmental conditions critical to the formation of severe convective weather over the United States (US). This study uses ensembles of Earth system model simulations of future climate change, with and without hypothetical SAI deployment, to examine possible future changes in thermodynamic and kinematic parameters critical to the formation of severe weather during convectively active seasons over the US Results show that simulated forced changes in thermodynamic parameters are significantly reduced under SAI relative to a climate change (SSP2‐4.5) world, while simulated changes in kinematic parameters are more difficult to distinguish. Also, unforced internal climate variability is likely to significantly modulate the projected forced climate changes over large regions of the US. Plain Language Summary: Human‐caused climate warming is projected to increase occurrences of environmental conditions which favor the formation of severe weather, such as thunderstorms. Stratospheric aerosol injection (SAI) is a form of climate intervention which aims to cool the planet by injecting sunlight‐reflecting particles into the stratosphere, a part of the upper atmosphere. This study investigates how SAI might impact future projections of the environmental conditions which favor the formation of severe weather. One set of climate models simulations show that future increases of the occurrence of temperature driven environmental characteristics that favor the formation of severe weather could be avoided if SAI were to be implemented. These simulations also show that future projections of environmental characteristics that favor the formation of severe weather that are dynamically driven may not be significantly impacted by the implementation of SAI. Climate variability that is not human‐driven may also significantly impact future projections of the environmental conditions which favor severe weather formation, whether or not SAI has been implemented. Key Points: Thermodynamic parameters relevant to severe weather formation are projected to increase in magnitude over the US due to climate changeStratospheric aerosol injection may prevent many of these projected increasesInternal decadal‐scale climate variability is likely to impact future projections of regional changes in convective weather environments [ABSTRACT FROM AUTHOR]

Abstract Continued climate warming, together with the overall evaluation and implementation of a range of climate mitigation and adaptation approaches, has prompted increasing research into proposed solar climate intervention methods, such as stratospheric aerosol injection (SAI). SAI would use aerosols to reflect a small amount of incoming solar radiation away from Earth to stabilize or reduce future warming due to increasing greenhouse gas concentrations. Research into the possible risks and benefits of SAI relative to the risks from climate change is emerging. There is not yet, however, an adequate understanding of how SAI might impact human and natural systems. For instance, little to no research to date has examined how SAI might impact environmental conditions critical to the formation of severe convective weather over the United States (US). This study uses ensembles of Earth system model simulations of future climate change, with and without hypothetical SAI deployment, to examine possible future changes in thermodynamic and kinematic parameters critical to the formation of severe weather during convectively active seasons over the US Results show that simulated forced changes in thermodynamic parameters are significantly reduced under SAI relative to a climate change (SSP2‐4.5) world, while simulated changes in kinematic parameters are more difficult to distinguish. Also, unforced internal climate variability is likely to significantly modulate the projected forced climate changes over large regions of the US.

Single‐model initial‐condition large ensembles are powerful tools to quantify the forced response, internal climate variability, and their evolution under global warming. Here, we present the CMIP6 version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) with currently 30 realizations for the historical period and five emission scenarios. The power of MPI‐GE CMIP6 goes beyond its predecessor ensemble MPI‐GE by providing high‐frequency output, the full range of emission scenarios including the highly policy‐relevant low emission scenarios SSP1‐1.9 and SSP1‐2.6, and the opportunity to compare the ensemble to complementary high‐resolution simulations. First, we describe MPI‐GE CMIP6, evaluate it with observations and reanalyzes and compare it to MPI‐GE. Then, we demonstrate with six application examples how to use the power of the ensemble to better quantify and understand present and future climate extremes, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI‐GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heatwaves would only avoid reaching 1–2 years return periods in 2071–2100 with low emission scenarios, that recently observed European precipitation extremes are captured only by complementary high‐resolution simulations, and that 3‐hourly output projects a decreasing activity of storms in mid‐latitude oceans. Further, the ensemble is ideal for estimates of probabilities of crossing global warming limits and the irreducible uncertainty introduced by internal variability, and is sufficiently large to be used for infilling surface temperature observations with artificial intelligence. Plain Language Summary: Climate model simulations that start from different initial states and differ only due to the chaos in the climate system are used extensively to quantify the forced climate response, variability intrinsic to the climate system, and their change under global warming. Here, we present a new version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) that is run as part of the latest generation of climate models. This single‐model ensemble currently consists of 30 realizations for the historical period 1850–2014 and for five scenarios of possible future climates until 2100. The power of MPI‐GE CMIP6 goes beyond its predecessor by not only providing monthly mean but also 3‐hourly to daily model output, the full range of future scenarios including the two highly policy‐relevant scenarios that were designed to match the Paris Agreement global warming limits of 1.5 and 2°C, and the opportunity to compare the low‐resolution ensemble to simulations of the same model version with higher horizontal resolution. In this paper, we describe the new ensemble and demonstrate with application examples how to use its power. Key Points: The Max Planck Institute Grand Ensemble in its CMIP6 version (MPI‐GE CMIP6) is a 30‐member initial‐condition large ensemble with up to 3‐hourly model output and five emission scenariosThe ensemble is specifically suited to investigate climate extremes and Paris Agreement global warming limitsMPI‐GE CMIP6 adequately represents heat extremes, while precipitation extremes are captured by complementary high‐resolution simulations [ABSTRACT FROM AUTHOR]

Abstract Single‐model initial‐condition large ensembles are powerful tools to quantify the forced response, internal climate variability, and their evolution under global warming. Here, we present the CMIP6 version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) with currently 30 realizations for the historical period and five emission scenarios. The power of MPI‐GE CMIP6 goes beyond its predecessor ensemble MPI‐GE by providing high‐frequency output, the full range of emission scenarios including the highly policy‐relevant low emission scenarios SSP1‐1.9 and SSP1‐2.6, and the opportunity to compare the ensemble to complementary high‐resolution simulations. First, we describe MPI‐GE CMIP6, evaluate it with observations and reanalyzes and compare it to MPI‐GE. Then, we demonstrate with six application examples how to use the power of the ensemble to better quantify and understand present and future climate extremes, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI‐GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heatwaves would only avoid reaching 1–2 years return periods in 2071–2100 with low emission scenarios, that recently observed European precipitation extremes are captured only by complementary high‐resolution simulations, and that 3‐hourly output projects a decreasing activity of storms in mid‐latitude oceans. Further, the ensemble is ideal for estimates of probabilities of crossing global warming limits and the irreducible uncertainty introduced by internal variability, and is sufficiently large to be used for infilling surface temperature observations with artificial intelligence.

Global warming is a gradual increase in the Earth's temperature generally due to the greenhouse effect caused by increased levels of carbon dioxide. Global warming has had significant consequences for human life, significantly affecting agricultural production, ecosystems, and water resources. The climate system of the Earth responds to a perturbation to the top of the atmosphere radiative balance through a change in temperature. This imbalance constitutes a radiative forcing of the climate system, and the magnitude of the response is determined by the strength of the forcing and the net radiative feedback. Equilibrium Climate Sensitivity (ECS) is an estimate of the eventual steady-state global warming at double CO2 and Transient Climate Response (TCR) is the mean global warming predicted to occur around the time of doubling CO2 in GCM and ESM runs for which atmospheric CO2 concentration is prescribed to increase at 1% per year. This study aimed to evaluate the performance of Climate Model Intercomparison Project Phase 6 (CMIP6) models by selecting 30 models considering TCR and ECS and to investigate temperature spatial distribution and annual trends in Iran. The performance of these models has been investigated with data from Iran's fifty-one synoptic stations for the historical period (1980-2014) using the Taylor diagram and box plot. The results showed that most CMIP6 models have good performance in simulating spatial temperature patterns. However, in the area-averaged, 73% of the selected models have estimated the temperature of the country as less than the station data. In general, more than 56% of the models showed a correlation higher than 0.5 compared to station data in the area-averaged temperature of Iran. Four models including CanESM5, INM-CM5-0, TaiESM1, and UKESM1-0-LL have shown the highest performance in estimating the temperature in Iran. The area-averaged annual temperature trend, which was examined by the modified Mann-Kendall test, showed that the temperature trend of CMIP6 models is increasing along with the observational data for all models. Most CMIP6 models, however, have simulated higher warming rates in the historical period, which differs from station data. These differences cannot be explained by internal climate variability (ICV), and the equilibrium climate sensitivity of most CMIP6 models has created a greater rate of warming in the models. For example, models such as CanESM5 and UKESM1-0-LL, which showed the highest trend, had the highest ECS and TCR among all. [ABSTRACT FROM AUTHOR]

Many studies have proved that hydrological extreme values estimated from decadal observation data and river inundation simulations are associated with various uncertainties; however, few studies have evaluated the uncertainties associated with internal climate variability. We used largeensemble river inundation simulations to quantitatively evaluate uncertainties in river depth at the Takahama monitoring station and flood extent in the Yodo River basin. Using a single 60-year ensemble, the river depth for a 1,000-year return period (RP) flood scale have uncertainty between --11.7% and +9.2% in a 3,000-year flood simulation. Thus, the RP of the simulated river depth ranges from 207-3,441 years. To maintain the RP uncertainty within ±300 years would require a simulation of ≥1,200 years. The flood extent uncertainty with an RP of 1,000 years was found to be -8.4% and +7.6% based on a 3,000-year simulation for the lower Yodo River basin. According to this result, the RP of the simulated flood extent ranges from 340-3,060 years. These results suggest that the decadal data used in conventional flood risk analyses potentially contain large uncertainty related to internal climate variability in the RP for water depth and flood extent by approximately 0.3-3-fold. [ABSTRACT FROM AUTHOR]

Internal climate variability (ICV) often violates the assumptions of statistical methods, and the climate research community does not have an established approach for addressing resulting biases. Here we argue for a technique we call climate model Large-Ensemble Monte-Carlo (LENS-MC) to inform the selection of statistical methods for real-world application. Until now, scientists have often made best efforts to select methods based on assumptions about the mathematical properties of ICV. LENS-MC relaxes these assumptions and justifies method selection, potentially for a wide range of statistical analyses. We demonstrate LENS-MC using a case study of statistical errors in 20 year trends in global temperature and top-of-atmosphere flux series, comparing results with standard ordinary least squares (OLS). OLS commonly underestimates trend uncertainties, resulting in a higher likelihood of falsely reporting statistically significant trends or changes in trends, for example reporting p < 0.05 in 20 year temperature trends when the statistics are actually equivalent to p < 0.56. LENS-MC tests result in the selection of methods that almost eliminate the low bias in OLS trend standard errors. Using the suggested methods, researchers are less likely to mistakenly report significant trends, and LENS-MC could be widely applied to statistical climate analysis for which model output is available, provided that model ICV displays similar statistical structure, such as in autocorrelation, to observed ICV.

The response of the Asian Summer Monsoon (ASM) circulation to the Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) forcing scenarios is examined using the CESM1 state-of-the-art global circulation model from 2021 to 2050. The projections show that monsoon precipitation will increase over East Asia, the North Pacific Ocean, the Indian Peninsula, and the Bay of Bengal under the RCP4.5 scenario. Conversely, the South Indian Ocean, West Asia, the Middle East, and the Central Pacific Ocean exhibit a decreasing trend in precipitation. Under the RCP8.5 scenario, precipitation is projected to increase over a wider swath of the Indian Ocean and the Middle East Asia. In the RCP4.5 scenario, the low-level wind circulation is likely to strengthen over the entire northern Indian Ocean, extending to the South China Sea, thereby increasing moisture transport from the Indian Ocean to peninsular India and the South China Sea. Conversely, in the RCP8.5 scenario, easterly winds strengthen over the South Indian Ocean, leading to an increase in moisture transport from the equatorial West Pacific Ocean to the Indian Ocean. A weak (strong) cyclonic circulation in response to the east-centered (west-centered) low sea level pressure trend over the North Pacific in RCP4.5 (RCP8.5) scenario is projected to help maintaining a strong (weak) ASM circulation from the India to east Asia. Internal climate variability is also calculated, revealing that the North Pacific Ocean near the Bering Sea is likely to play a dominating role and contribute significantly to the future ASM dynamics. In both scenarios, internal variability is found to substantially contribute to changes in monsoon circulation over the Indian Ocean.

In the 20th century, with the intensification of human activities, the Earth is experiencing unprecedented warming. However, there are certain differences in the sensitivity of temperature changes to anthropogenic forcings in different regions and at different altitudes of the troposphere. The time of emergence (TOE) is the key point at which the anthropogenic climate change signal exceeds from the internal climate variability serving as a noise. It is a crucial variable for climate change detection, climate prediction and risk assessment. Here, we systematically analyzed the spatiotemporal characteristics of the TOE of temperature changes over the past century by calculating the SNR based on the selected CMIP6 multi-model outputs. The results show that the temperature TOE, particularly in the lower and middle troposphere, shows distinct latitude dependence, displaying an ‘M-type’ distribution from the Antarctic to the Arctic: it first appears in low-latitudes, followed by high-latitudes, and last appears in the two mid latitude bands. For the tropics, the TOE of tropospheric temperatures becomes earlier with increasing altitude: the TOE of air temperatures at the surface, mid-tropospheric 500 hPa and upper-tropospheric 200 hPa occurs in 1980 ± 15, 1965 ± 20, and 1930 ± 30, respectively. The TOEs of tropospheric temperatures in eastern equatorial Pacific are 10–30 years later than those in the western equatorial Pacific. For the regional TOEs of surface air temperature diverse differences exist on land and ocean in various latitudes of two hemispheres.

Roles of internal climate variabilities regulating global and ocean temperature changes is a hot but complex issue of scientific concern, influencing the comprehensive policy-making in response to global and regional warming. In this study, the time series of monthly global and ocean mean surface temperature (GST and OST, respectively) since 1866 is successfully reconstructed via natural and anthropogenic forcing factors and internal climate variability by using a Multi-Layer Perceptron (MLP) neural network technique. The MLP demonstrates prominent monthly GST and OST reconstruction skills on both interannual and annual time scales. Most of the warming in GST and OST since 1866 is found to be attributable to anthropogenic forcing, while the multidecadal and interannual GST and OST variations are considerably dominated by Atlantic Multidecadal Oscillation (AMO). Internal climate variabilities like Interdecadal Pacific Oscillation (IPO) can amplify the GST and OST changes and explain the global warming slowdown since 1998. Southern Oscillation Index (SOI) performs a similar role as IPO but to a lesser extent. Changes in OST caused by solar forcing are more considerable than those in GST. Moreover, the ‘biased warmth’ during the Second World War is successfully reconstructed in MLP. AMO and IPO can explain most annual and even sub-annual temperature variations during this period, offering an explanation for the existence of this abnormal warm period other than that it was entirely caused by instrumental errors. The generally high accuracy of reconstructions on interannual and annual time scales can enhance the ability to monitor the prompt feedback of specific external radiative forcings and internal variabilities to changes in climate.

Changes in Arctic sea ice since the middle of the last century are explored in this study. Both observations and climate model simulations show an overall sea ice expansion during 1953–1970 but a general sea ice decline afterward. Anthropogenic aerosols, nature forcing and atmospheric ozone changes are found to contribute to the sea ice expansion in the early period. Their effects are strong generally in late boreal summer. On the other hand, greenhouse gas warming has a dominant effect on diminishing Arctic sea ice cover during 1971–2005, especially in September. Internal climate variability also plays a role in the Arctic sea ice change during 1953–1970. However, it cannot solely explain the Arctic sea ice decline since the 1970s. [ABSTRACT FROM AUTHOR]

The Arctic is especially vulnerable to climate change and is warming faster than the global average. Changes in this region pose a heightened threat due to the immense amount of carbon frozen as organic material in the soil. When permafrost thaws, organic material decays and releases as greenhouse gases into the atmosphere, contributing to further warming. A better understanding of the processes that influence permafrost degradation is needed to inform climate adaptation and mitigation planning. This study assesses changes to Arctic permafrost across 35 ensemble member simulations from the Community Earth System Model 1 Large Ensemble Project, under the Representative Concentration Pathway 8.5 21st century scenario. Most Arctic near‐surface permafrost is lost by 2100, but timing varies across regions and with soil depth. Internal climate variability, represented by differences between ensemble members, has a constrained influence on degradation timing due to relative consistency of summer temperature increases and insulation by winter snow cover. Plain Language Summary: The Arctic contains a large amount of soil that stays frozen year‐round, called permafrost. As the Arctic warms in response to human caused climate change this soil is beginning to thaw, which has regional and global implications. Regionally, permafrost degradation can make the ground unstable, with potential to damage infrastructure such as roads, bridges, and structures. These frozen soils also contain a significant amount of organic material, which can breakdown and be released into the atmosphere as carbon dioxide and methane when the ground thaws. Globally, these greenhouse gases contribute to further warming and exacerbating climate change. Therefore, it is critical to understand how quickly the Arctic is warming and the processes that influence the timing of permafrost loss. In this study, we use a global Earth system model to explore the role of internal climate variability in influencing timing of permafrost thaw. We assess 35 simulations of the 21st century that differ only in their realization of variability. We find a steady decline in Arctic‐wide permafrost that is consistent in timing across the different simulations. Regionally, the thaw timing can vary up to ∼10 years, but is constrained by lower variability in summer surface temperatures and winter snow cover. Key Points: The timing of Arctic permafrost thaw in Community Earth System Model 1 Representative Concentration Pathway 8.5 experiments is dependent on latitude, elevation, soil depth, and internal variabilityRelatively consistent warming across ensemble members in summer limits the impact of internal variability on the timing of permafrost thawArctic‐wide permafrost loss has little variation across ensemble members, implying small differences in potential greenhouse gas emissions [ABSTRACT FROM AUTHOR]

Near‐term prediction of tropical cyclone (TC) activity is of great importance to formulate the long‐term plans for mitigating the potential TC‐induced damages. Here, we introduce a multi‐timescale regression model of TC genesis frequency (TCGF), which includes contributions by three interannual modes (El Niño‐Southern Oscillation, sea surface temperature anomalies over the eastern Indian Ocean and tropical North Atlantic), two interdecadal modes (the Atlantic Multidecadal Oscillation and the Interdecadal Pacific Oscillation), and a global warming mode. The model is shown to be able to capture well the present‐day multi‐timescale changes in TCGF in the major TC basins in the Northern Hemisphere. By combining the model and 100‐member simulations by the Max Planck Institute Earth System Model, we predict a robust increase in TCGF over the eastern North Pacific, an insignificant increase over the western North Pacific, and little change over the North Atlantic during 2020–2030 relative to 2009–2019. Plain Language Summary: How tropical cyclone (TC) activity will change in the next decade or so is especially important for government policymakers. However, the field on TC activity is very much in its infancy. Here we predict the near‐term changes in TC genesis frequency (TCGF) in the three main TC basins in the Northern Hemisphere, namely the western North Pacific, the eastern North Pacific, and the North Atlantic, by combining a new multi‐timescale regression model and the state‐of‐the‐art climate model projections. This regression model is constructed based on six key climate factors on different time scales, and can well reproduce the present‐day TCGF change during 1960–2019. The model predicts a significant increase in TCGF over the eastern North Pacific while insignificant change over the other two basins in the next 10 years. Key Points: We have developed a multi‐timescale regression model to represent changes in tropical cyclone (TC) genesis frequency (TCGF)The model can capture well the present‐day multi‐timescale changes in TCGF in the major TC basins in the Northern HemisphereThe model is used to conduct near‐term prediction of TCGF in the Northern Hemisphere by combining climate model projections [ABSTRACT FROM AUTHOR]

As anthropogenic activities warm the Earth, the fundamental solution of reducing greenhouse gas emissions remains elusive. Given this mitigation gap, global warming may lead to intolerable climate changes as adaptive capacity is exceeded. Thus, there is emerging interest in solar radiation modification, which is the process of deliberately increasing Earth’s albedo to cool the planet. Stratospheric aerosol injection (SAI)—the theoretical deployment of particles in the stratosphere to enhance reflection of incoming solar radiation—is one strategy to slow, pause, or reverse global warming. If SAI is ever pursued, it will likely be for a specific aim, such as affording time to implement mitigation strategies, lessening extremes, or reducing the odds of reaching a biogeophysical tipping point. Using an ensemble climate model experiment that simulates the deployment of SAI in the context of an intermediate greenhouse gas trajectory, we quantified the probability that internal climate variability masks the effectiveness of SAI deployment on regional temperatures. We found that while global temperature was stabilized, substantial land areas continued to experience warming. For example, in the SAI scenario we explored, up to 55% of the global population experienced rising temperatures over the decade following SAI deployment and large areas exhibited high probability of extremely hot years. These conditions could cause SAI to be perceived as a failure. Countries with the largest economies experienced some of the largest probabilities of this perceived failure. The potential for perceived failure could therefore have major implications for policy decisions in the years immediately following SAI deployment. [ABSTRACT FROM AUTHOR]

The Indian summer monsoon's (ISM) response to low (1.5 °C) and medium (2.0 °C) warming scenarios are examined during the period 2021–2050 using 11 member ensembles of the state-of-the-art Community Earth System Model, version 1 (CESM1-CAM5) datasets. We find that in the near-term period an increase in warming by 0.5 °C will strengthen the monsoon circulation and precipitation over the Indian landmass and east Asia. Under the 1.5 °C warming scenario, the ISM circulation will weaken, and precipitation will exhibit a decreasing trend, while the ISM precipitation and circulation will strengthen under the 2.0 °C warming scenario. The strengthening of ISM under the 2.0 °C warming scenario is due to the strengthening of the South China Sea (SCS) anticyclone and a high-pressure center near the Philippine Island. This high-pressure center over the SCS will facilitate a cross-basin strengthening of the easterly wind from the western Pacific Ocean/SCS to the equatorial Indian Ocean, which will, in turn, strengthen the transport of the Pacific-origin moisture to the Indian subcontinent and East Asia. This increase in moisture content over the Indian subcontinent will strengthen the monsoonal circulation and precipitation under the 2.0 °C warming scenario. [ABSTRACT FROM AUTHOR]

The impacts of the El Niño-Southern Oscillation (ENSO) are expected to change under increasing greenhouse gas concentrations, but the large internal variability of ENSO and its teleconnections makes it challenging to detect such changes in a single realization of nature. In this study, we explore both the internal variability and radiatively forced changes of boreal wintertime ENSO teleconnection patterns through the analysis of 30-member initial condition ensembles of the Seamless System for Prediction and EArth System Research (SPEAR), a coupled global climate model developed by the NOAA Geophysical Fluid Dynamics Laboratory. We focus on the projected changes of the large-scale circulation, temperature, and precipitation patterns associated with ENSO for 1951–2100 under moderate and high emissions scenarios (SSP2-4.5 and SSP5-8.5). We determine the time of emergence of these changes from the noise of internal climate variability, by determining the time when the amplitude of the ensemble mean change in the running 30-year ENSO composites first exceeds the 1951-1980 composite anomaly amplitude by at least one ensemble standard deviation. Overall, the high internal variability of ENSO teleconnection patterns primarily limits their expected emergence to tropical and subtropical regions before 2100, where some regions experience robust changes in ENSO-related temperature, precipitation, and 500 hPa geopotential height patterns by the middle of the twenty-first century. The earliest expected emergence generally occurs over tropical South America and Southeast Asia, indicating that an enhanced risk of ENSO-related extreme weather in that region could be detected within the next few decades. For signals that are expected to emerge after 2050, both internal climate variability and scenario uncertainty contribute similarly to a time of emergence uncertainty on the order of a few decades. We further explore the diversity of ENSO teleconnections within the SPEAR large ensemble during the historical period, and demonstrate that historical relationships between tropical sea surface temperatures and ENSO teleconnections are skillful predictors of projected changes in the Northern Hemisphere El Niño 500 hPa geopotential height pattern.

In this study, we define a metric for the intensity of internal climate variability (ICV) based on global surface temperature in the present climate and suggest that it can be used to understand the diversity of projected changes in ENSO amplitude in the future. We analyze both the 35-member Community Earth System Model Large Ensemble and the 30-members from Geophysical Fluid Dynamical Laboratory Large Ensemble from the present climate to future climate. While ENSO amplitude tends to decrease from the present climate to the end of 21st century in some ensemble member with a strong ICV during the present climate, it increases or stays the same in other ensemble members with a weak ICV. The result indicates that the intensity of ICV in the present climate in climate models may cause the difference of ENSO amplitude changes in a warmer world. Therefore, the intensity of ICV in the present climate should be cautiously examined in climate models to correctly project the ENSO amplitude changes in a changing climate.

This study assesses the effects of internal climate variability on wave height trend assessment using the d4PDF-WaveHs, the first single model initial-condition large ensemble (100-member) of significant wave height (Hs) simulations for the 1951–2010 period, which was produced using sea level pressure taken from Japan’s d4PDF ensemble of historical climate simulations. Here, the focus is on assessing trends in annual mean and maximum Hs. The result is compared with other model simulations that account for other sources of uncertainty, and with modern wave reanalyses. It is shown that the trend variability arising from internal climate variability is comparable to the variability caused by other factors, such as climate model uncertainty. This study also assesses the likelihood to mis-estimate trends when using only one ensemble member and therefore one possible realization of the climate system. Using single member failed to detect the statistically significant notable positive trend shown in the ensemble in some areas of the Southern Ocean. The North Atlantic Ocean is found to have large internal climate variability, where different ensemble-members can show trends of the opposite signs for the same area. The minimum ensemble size necessary to effectively reduce the risk of mis-assessing Hs trends is estimated to be 10; but this largely depends on the specific wave statistic and the region of interest, with larger ensembles being required to assess extremes. The results also show that wave reanalyses are not suitable for analyzing Hs trends due to temporal inhomogeneities therein, in agreement with recent studies.

• Climate variability explains 30–90% of flood variability across Sub-Saharan Africa. • East/southern African flood variability mainly driven by Pacific and Indian Oceans. • West/central African flood variability mainly driven by Atlantic and Mediterranean. • Flood occurrences could vary by ± 10–50 % from 2015 to 2100 due to climate variability. Sub-Saharan Africa (SSA) is strongly affected by flood hazards, endangering human lives and economic stability. However, the role of internal climate modes of variability in driving fluctuations in SSA flood occurrence remains poorly documented and understood. To address this gap, we quantify the relative and combined contribution of large-scale climate drivers to seasonal and regional flood occurrence using a new 65-year daily streamflow dataset, sea-surface temperatures derived from observations, and 12 Single Model Initial-condition Large Ensembles (SMILEs) from the Coupled Model Intercomparison Project Phases 5 and 6. We find significant relationships between floods and large-scale climate variability across SSA, with climatic drivers accounting for 30–90 % of the variability in floods. Notably, western, central, and the summer-rain region of southern Africa display stronger teleconnections to large-scale climate variability in comparison to East Africa and the winter-rain region of South Africa, where regional circulation patterns and human activities may play a more important role. In southern and eastern Africa, floods are mainly influenced by teleconnections with the Pacific and Indian Oceans, while in western and central Africa, teleconnections with the Atlantic Ocean and Mediterranean Sea play a larger role. We also find that the number of floods is projected to fluctuate by ± 10–50 % during the 21st century in response to different sequences of key modes of climate variability. We also note that the relative contributions of large-scale climate variability to future flood risks are generally consistent across all SMILEs. Our findings thus provide valuable information for long-term disaster risk reduction and management. [ABSTRACT FROM AUTHOR]

This study investigates the role of internal climate variability on the wintertime surface air temperature (SAT) trend over Eurasia during 1970–2004 based on outputs from the Community Earth System Model Large Ensemble Project. The SAT trends vary significantly in both spatial pattern and magnitude among ensemble members, indicating the essential role of internal climate variability on SAT trends. The leading modes of the spread of SAT trends are extracted via inter‐member empirical orthogonal function (EOF) analysis of the SAT trend patterns among 40 ensemble members. The first EOF mode shows widespread warming over northern Eurasia and slight cooling southward of 40°N during its positive phase. The second EOF mode displays a zonal dipole, with one center located in Europe and the other of the opposite sign over Siberia. The Arctic Oscillation and Scandinavian patterns are key dynamical factors associated with the two dominant modes, respectively. A dynamical adjustment method is further employed to decompose the internal climate variability into those related to large‐scale atmospheric circulation and residuals. The spread of SAT trends among ensemble members is primarily associated with the various large‐scale atmospheric circulation and second by the residual component that is closely tied to diverse snow cover trends over Eurasia, especially over northern Eurasia. These results may help understand the relationship between internal climate variability and winter SAT trends over Eurasia via dynamical and thermodynamical processes. Key Points: Internal climate variability plays a crucial role in the Eurasian winter surface air temperature trends of the past decadesArctic Oscillation and Scandinavian patterns are two essential internal dynamical factors associated with the temperature trendsSnow cover is the key internal thermodynamical factor associated with the temperature trends [ABSTRACT FROM AUTHOR]

This study examines the relationship between the Atlantic Multidecadal Variability (AMV) and the wintertime atmospheric circulation of the North Atlantic in simulations of the fifth Coupled Model Intercomparison Project (CMIP5). Comparisons of internal (using preindustrial control simulations) and externally forced (using historical and Representative Concentration Pathways 8.5 simulations) simulated AMV with observations suggest that the CMIP5 models lack internally generated AMV, except for two models (GFDL-ESM2G and HadGEM2-ES). A long-term influence of the winter North Atlantic Oscillation (NAO) on the AMV is identified, but no consistent feedback of the AMV onto the atmospheric circulation is found among the models. However, GFDL-ESM2G and HadGEM2-ES show a small lagged NAO signal that suggests a driving role of the ocean on decadal fluctuations of the atmosphere, with two different potential mechanisms. HadGEM2-ES exhibits a latitudinal shift of the Atlantic Intertropical Convergence Zone that can modulate the NAO through a Rossby wave train emanating from the tropics. In GFDL-ESM2G, the AMV is associated with a decrease in storm track activity and a shift of the intraseasonal weather regimes toward the negative NAO regime. These results raise hope that some long-term predictability of the winter climate over the North Atlantic and surrounding continents could be extracted from long-term oceanic fluctuations of the North Atlantic Ocean, provided that the AMV is correctly represented in coupled ocean-atmosphere models.

Large historical and future ensemble simulations from the Max-Planck Institute and the Canadian Earth System Models and from CMIP5 have been analysed to investigate the uncertainty due to internal variability in multi-decadal temperature and precipitation trends over Europe. Internal variability dominates the uncertainties in temperature and precipitation trends in all seasons at 30-year time scales. Locally, seasonal 30-year temperature trends deviate up to ±3 °C from the ensemble mean trend. Thus, in the entire of Europe, local seasonal temperature changes until year 2050 from below −1 °C up to more than 4 °C are possible according to the model results. Up to 30% of all ensemble members show negative temperature trends until year 2050 in winter, up to 10% of the members in summer. Uncertainties of 30-year precipitation trends due to internal variability exceed the trends almost everywhere in Europe. Only in few European regions more than 75% of the members agree on the sign of the change until year 2050. In southern Sweden, minimum and maximum winter (summer) temperature trends in the next 30 years differ with up to 7 °C (5 °C) between individual members of the large model ensembles. Large positive temperature trends are linked to positive (negative) precipitation trends in winter (summer) in southern Sweden. This variability is attributed to the variability in large scale atmospheric circulation trends, mainly due to internal atmospheric variability. We find only weak linkages between the variability of temperature trends and the dominant decadal to multi-decadal climate modes. This indicates that there is limited potential to predict the multi-decadal variability in climate trends. The main findings from our study are robust across the large ensembles from the different models used in this study but at the local scale, the results depend also on the choice of the model.

Changes in Arctic sea ice since the middle of the last century are explored in this study. Both observations and climate model simulations show an overall sea ice expansion during 1953–1970 but a general sea ice decline afterward. Anthropogenic aerosols, nature forcing and atmospheric ozone changes are found to contribute to the sea ice expansion in the early period. Their effects are strong generally in late boreal summer. On the other hand, greenhouse gas warming has a dominant effect on diminishing Arctic sea ice cover during 1971–2005, especially in September. Internal climate variability also plays a role in the Arctic sea ice change during 1953–1970. However, it cannot solely explain the Arctic sea ice decline since the 1970s.

Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long‐term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different sampling of internal climate variability. We further demonstrate the validity of our statistical approach by recreating an ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long‐term trends in the presence of internal variability and find a wider range of possible trends in chlorophyll due to the sampling of internal variability in subpolar regions than in subtropical regions. Key Points: We statistically emulate the internal variability in the historical record of chlorophyll observationsThe resulting synthetic ensemble of observed chlorophyll is comparable to a large ensemble of an Earth system modelThe synthetic ensemble reveals a wider range of internal trends in subpolar regions than subtropical regions [ABSTRACT FROM AUTHOR]

By analyzing large ensemble simulations using the Community Earth System Model (CESM_LE), the Max Planck Institute Earth System Model Grand Ensemble (MPI_GE), and Coupled Model Intercomparison Project phase 5 (CMIP5) climate models, we quantified internal climate variability (ICV) of surface temperature in each model based on the spread of simulated global mean surface temperature from the ensemble mean. Then, we examined the characteristics of simulated surface temperature variability in climate models with large and small ICV in the present climate and in a future climate. Both the CESM_LE and MPI_GE members with large ICVs tended to simulate larger surface temperature variability at low latitudes, including El Niño and Southern Oscillation (ENSO) variability, and larger cooling and warming trends of the global mean surface temperatures than those with small ICVs in the present climate. Similar characteristics were observed in CMIP5 climate models with large and small ICVs in the present climate. This implies that surface temperature variability including extreme climate events should be cautiously examined in climate models with large and small ICVs. On the other hand, the characteristics of surface temperature variability simulated in the CMIP5 climate models with large or small ICVs were similar from the present climate to future climate with magnitude of ICVs. This was in contrast to that simulated in the CESM_LE and MPI_GE, in which the magnitude of ICV changes between the present climate and the future climate. We inferred that these differences between CMIP5 climate models and large ensemble simulations could primarily be attributed to intermodel differences in the CMIP5 climate models, including model physics and parameterizations. [ABSTRACT FROM AUTHOR]

As a typical inland arid and semiarid region, Central Asia (CA) is vulnerable to the forced global warming (FGW) due to anthropogenic activity. Aiming at the interdecadal variation of the FGW-forced aridity pattern (FAP) in CA, we try to extract the associated oceanic and atmospheric modes by analyzing observations, reanalysis data and multi-model simulations during 1950–2016. The FAP in CA features a tripolar pattern with wetting–drying-wetting responses arranging from southeast to northwest and shows strong interdecadal-to-interannual amplitude variations. It is found that the sea surface temperature (SST) in the tropical South Atlantic (TSA) well correlates with the amplitude variation of FAP on interdecadal time scale, possibly through modulating the interannual SST modes characterized by the North Atlantic horseshoe-like dipole (NAHD) and the El Ninõ and South Oscillation (ENSO). Corresponding to the enhancing FAP from the middle 1970s to early 2000s, the TSA-modulated NAHD and ENSO, together with the Pacific Decadal Oscillation-modulated Indian Ocean Dipole-like mode, show connections with an Eurasian middle-latitude wave train coupled with the North Arctic Oscillation and equatorial low, which favors the moisture transport to strengthen the tripolar FAP by forming a local circulation dipole with positive/negative anomaly over the northwest/southeast CA. But after the early 2000s, the increasing FAP amplitude is decelerated due to the interdecadal decline of TSA accompanied by the weakened/reversed relationship between FAP and the NAHD/ENSO. Because of the corresponding breakdown of the wave train, the favorable local circulation is unavailable to support the sustained enhancement of FAP. Therefore, the multiscale coupling between the above oceanic and atmospheric modes is significantly related to the characteristic of stage of the forced aridity change in CA under the background of global warming. [ABSTRACT FROM AUTHOR]

Extratropical cyclones, a major phenomenon of the mid‐latitude atmospheric dynamics, show strong variability over a range of time scales. Future projections hint at an increase of cyclonic intensity and the associated precipitation, an important fact to be considered when developing future risk assessments. This review presents a first overview of studies which (a) puts the current variability and projected future climate changes of extratropical cyclone characteristics in a long‐term perspective, (b) shows connections to natural external forcings, and (c) deepens our understanding of cyclone intensification processes for past climate periods. We summarize the current state of knowledge for two periods in the past—the last millennium and the Last Glacial Maximum (LGM, 21,000 years ago). For these two periods, the sparse information from paleo proxy archives are compared to climate modeling results on global and regional scales. For example, strong changes of the climate mean state, induced by orbital forcing and associated feedbacks, show strong effects on different cyclone characteristics, for example, a southward shift of the storm tracks over the North Atlantic during the LGM. Other findings indicate that dynamic processes could play at least an equally important role as thermodynamic processes for the variations of cyclone‐induced precipitation. This is in contrast to the projected future changes in cyclone‐related precipitation, which are driven primarily by thermodynamic processes. The review demonstrates how a paleoclimatic view can foster an extended process understanding and be instrumental to better understand future changes in extratropical cyclones and associated characteristics. This article is categorized under:Paleoclimates and Current Trends > Modern Climate Change [ABSTRACT FROM AUTHOR]

Large historical and future ensemble simulations from the Max-Planck Institute and the Canadian Earth System Models and from CMIP5 have been analysed to investigate the uncertainty due to internal variability in multi-decadal temperature and precipitation trends over Europe. Internal variability dominates the uncertainties in temperature and precipitation trends in all seasons at 30-year time scales. Locally, seasonal 30-year temperature trends deviate up to ±3 °C from the ensemble mean trend. Thus, in the entire of Europe, local seasonal temperature changes until year 2050 from below −1 °C up to more than 4 °C are possible according to the model results. Up to 30% of all ensemble members show negative temperature trends until year 2050 in winter, up to 10% of the members in summer. Uncertainties of 30-year precipitation trends due to internal variability exceed the trends almost everywhere in Europe. Only in few European regions more than 75% of the members agree on the sign of the change until year 2050. In southern Sweden, minimum and maximum winter (summer) temperature trends in the next 30 years differ with up to 7 °C (5 °C) between individual members of the large model ensembles. Large positive temperature trends are linked to positive (negative) precipitation trends in winter (summer) in southern Sweden. This variability is attributed to the variability in large scale atmospheric circulation trends, mainly due to internal atmospheric variability. We find only weak linkages between the variability of temperature trends and the dominant decadal to multi-decadal climate modes. This indicates that there is limited potential to predict the multi-decadal variability in climate trends. The main findings from our study are robust across the large ensembles from the different models used in this study but at the local scale, the results depend also on the choice of the model. [ABSTRACT FROM AUTHOR]

Internal climate variability co‐exists with anthropogenic climate change and places limits on the accuracy of regional climate projections due to its inherent unpredictability. This "certain" uncertainty in regional projections introduced by internal variability contrasts with uncertainty resulting from structural differences amongst climate models, which is potentially reducible as climate models improve. Initial‐condition "Large Ensembles" of simulations with individual climate models provide a new perspective on the expected range of future climate change outcomes. Their value for climate risk assessment, adaptation management, and decision‐making has yet to be fully realized. Key Points: Internal climate variability introduces unavoidable uncertainty in regional climate projections due to its limited predictabilityInitial‐condition "Large Ensembles" conducted with global coupled climate models provide an expected range of future climate outcomesTheir value for climate risk assessment, adaptation management, and decision‐making has yet to be fully realized [ABSTRACT FROM AUTHOR]

High- and low pressure systems of the large-scale atmospheric circulation in the mid-latitudes drive European weather and climate. Potential future changes in the occurrence of circulation types are highly relevant for society. Classifying the highly dynamic atmospheric circulation into discrete classes of circulation types helps to categorize the linkages between atmospheric forcing and surface conditions (e.g. extreme events). Previous studies have revealed a high internal variability of projected changes of circulation types. Dealing with this high internal variability requires the employment of a single-model initial-condition large ensemble (SMILE) and an automated classification method, which can be applied to large climate data sets. One of the most established classifications in Europe are the 29 subjective circulation types called Grosswetterlagen by Hess & Brezowsky (HB circulation types). We developed, in the first analysis of its kind, an automated version of this subjective classification using deep learning. Our classifier reaches an overall accuracy of 41.1% on the test sets of nested cross-validation. It outperforms the state-of-the-art automatization of the HB circulation types in 20 of the 29 classes. We apply the deep learning classifier to the SMHI-LENS, a SMILE of the Coupled Model Intercomparison Project phase 6, composed of 50 members of the EC-Earth3 model under the SSP37.0 scenario. For the analysis of future frequency changes of the 29 circulation types, we use the signal-to-noise ratio to discriminate the climate change signal from the noise of internal variability. Using a 5%-significance level, we find significant frequency changes in 69% of the circulation types when comparing the future (2071–2100) to a reference period (1991–2020).

Drought is one of the extreme events that can be caused by internal climate variability (ICV) and external forcing (EF). Here, the authors investigate the relative contributions of ICV and EF to meteorological drought changes in China using 40 members from the Community Earth System Model Large Ensemble (CESE_LE) project for historical simulations (in response to greenhouse gases and other EF) and future simulations under the RCP8.5 scenario. The authors use the Standardized Precipitation Index (SPI) to represent meteorological drought, and then define and analyze four drought parameters (frequency, severity, duration, and maximum duration) over eight regions of China. For historical periods, the ICV plays a dominant role in drought variation, while with global warming under the RCP8.5 scenario the EF becomes the prominent factor for drought characteristics. With the global warming signal, the effect of ICV varies with the drought parameters. This study suggests that the ICV should be taken into account when climate model simulations are used to investigate drought—in particular, for historical periods.

The main focus of this study is the zonal contrast of the Sahel precipitation shown in the CMIP5 climate projections: precipitation decreases over the western Sahel (i.e., Senegal and western Mali) and increases over the central Sahel (i.e., eastern Mali, Burkina Faso and Niger). This zonal contrast in future precipitation change is a robust model response to climate change but suffers from a lack of an explanation. To this aim, we study the impact of current and future climate change on Sahel precipitation by using the Large Ensemble of the Community Earth System Model version 1 (CESM1). In CESM1, global warming leads to a strengthening of the zonal contrast, as shown by the difference between the 2060–2099 period (under a high emission scenario) and the 1960–1999 period (under the historical forcing). The zonal contrast is associated with dynamic shifts in the atmospheric circulation. We show that, in absence of a forced response, that is, when only accounting for internal climate variability, the zonal contrast is associated with the Pacific and the tropical Atlantic oceans variability. However, future patterns in sea surface temperature (SST) anomalies are not necessary to explaining the projected strengthening of the zonal contrast. The mechanisms underlying the simulated changes are elucidated by analysing a set of CMIP5 idealised simulations. We show the increase in precipitation over the central Sahel to be mostly associated with the surface warming over northern Africa, which favour the displacement of the monsoon cell northwards. Over the western Sahel, the decrease in Sahel precipitation is associated with a southward shift of the monsoon circulation, and is mostly due to the warming of the SST. These two mechanisms allow explaining the zonal contrast in precipitation change. [ABSTRACT FROM AUTHOR]

Previous studies indicated that the wintertime North Pacific Oscillation (NPO) could exert marked impacts on the following winter El Niño-Southern Oscillation (ENSO) via the seasonal footprinting mechanism (SFM). Here, we examine this winter NPO-ENSO relationship in a 50-member ensemble of historical simulations conducted with the Canadian Centre for Climate Modeling and Analysis second generation Canadian Earth System Model (CanESM2) over the period of 1950–2005. The observed NPO pattern, featured by a meridional dipole atmospheric anomaly over the North Pacific, can be well reproduced by all of the 50 ensemble members. The multi-member ensemble (MME) mean can well simulate the observed NPO-ENSO relationship, as well as the SFM process. However, there exists a large spread of the results among the 50 members due to internal climate variability. Internal climate variability influences the winter NPO-ENSO relationship through modulating the subtropical center of the NPO. Specifically, the ensemble members with high NPO-ENSO correlations tend to have strong atmospheric anomalies over the subtropical North Pacific in winter. The atmospheric circulation anomaly brings strong sea surface temperature and precipitation anomalies in the tropical central Pacific and westerly wind anomalies over the tropical western Pacific in the following spring. These anomalies sustain in the following seasons and eventually lead to ENSO events in the following winter. [ABSTRACT FROM AUTHOR]

This study investigates the influences of internal climate variability (ICV) on Eurasian spring (March–May) surface air temperature (SAT) trends based on a 30-member ensemble of the Community Earth System Model version 1 (CESM1) from 1979 to 2014. The SAT trends over the mid-high latitude Eurasia vary considerably among individual simulations, indicating a large impact of ICV on SAT trends at regional scales. The leading pattern of internally generated SAT trend variability over Eurasia among the 30 SAT trends displays same sign distribution over the mid-high latitudes of Eurasia. This pattern is associated with the Northern Hemisphere annular mode (NAM) that accounts for about 1/3 of total variance of the spring sea level pressure (SLP) trends. The second pattern of internally generated SAT trend variability features a dipole structure, accompanied by prominent circulation changes over the North Atlantic and Europe. Internal dynamics account for approximately 40–70% of the observed warming over the central Eurasia and the Russian Far East. A dynamical adjustment methodology is employed to remove the effects of internal atmospheric circulation variability. Dynamical adjustment greatly reduces the spread of SAT trends within the ensemble and brings both the simulated and observed trends closer to the ensemble mean. Changes in the Barents–Kara Sea ice and Eurasian snow cover contribute to the remaining spread in SAT trends over the north coast of Russia and in the region north of the Caspian Sea, respectively. Both dynamics and thermodynamics contribute to the Barents–Kara Sea ice and Eurasian snow cover trends. [ABSTRACT FROM AUTHOR]

Previous observational and modeling studies indicate that the wintertime North Pacific Oscillation (NPO) could significantly impact the following winter El Niño-Southern Oscillation (ENSO) variability via the seasonal footprinting mechanism (SFM). This study explores climate projections of this winter NPO-ENSO relation in a warming climate based on a 50-member large ensemble of climate simulations conducted with the second-generation Canadian Earth System Model (CanESM2). The ensemble mean of the 50 members can well reproduce the observed winter NPO pattern, the NPO-ENSO relationship, and the SFM process over the historical period 1950–2003. These 50 members are then employed to examine climate projections of the NPO-ENSO connection over the anthropogenic forced period 2020–2073. Results indicate that there exists a large spread of projected NPO-ENSO connections across these 50 ensemble members due to internal climate variability. Internal climate variability brings uncertainties in the projection of the winter NPO-ENSO connection originally seen in projected changes of the subtropical center of the winter NPO. The spread of projections of winter NPO-associated atmospheric anomalies over the subtropical North Pacific further results in various responses in the projections of winter and spring precipitation anomalies over the tropical North Pacific, as well as spring zonal wind anomalies over the tropical western Pacific, which eventually lead to uncertainties in the projection of the sea surface temperature anomalies in the tropical central-eastern Pacific from the following summer to winter. [ABSTRACT FROM AUTHOR]

Bias correction methods have now emerged as the most commonly used approach when applying climate model outputs to impact studies. However, comparatively much fewer studies have looked at the limitations of bias correction caused by the very nature of the climate system. Two main sources of errors can affect the efficiency of bias correction over a future period: climate sensitivity and internal variability of the climate system. The former is related to differences in the forcing response between a climate model and the real climate system, whereas the latter results from the chaotic nature of the climate system. Using a "pseudo-reality" approach, this study investigates the contribution of these two sources of error to remaining biases of climate model after bias correction for future periods. The pseudo-reality approach uses one climate model as a reference dataset to correct other climate models. Results indicate that bias correction is beneficial over the reference period and in near future periods. However, large biases remain in future periods. The difference in climate sensitivities is the main contributor to the remaining biases in corrected data. Internal variability affects the near and far future similarly and may dominate in the near future, especially for precipitation. The impact of differences in climate sensitivity between the reference dataset and climate model data cannot be eliminated, while the impact of internal variability can be lessened by using a reference period for as long as possible to filter out low-frequency modes of variability. [ABSTRACT FROM AUTHOR]

Policy makers need reliable future climate projection for adaptation purposes. A clear separation of sources of uncertainty also helps narrow the projection uncertainty. However, it remains unclear for monsoon precipitation projections. Here we quantified the contributions of internal variability, model uncertainty, and scenario uncertainty to the ensemble spread of global land monsoon precipitation projections using Coupled Model Intercomparison Project Phase 5 (CMIP5) models and single‐model initial‐condition large ensembles (SMILEs). For mean precipitation, model uncertainty (contributing ~90%) dominates the projection uncertainty, while the contribution of internal variability (scenario uncertainty) decreases (increases) with time. The source of uncertainty for extreme precipitation differs from that of mean precipitation mainly in long‐term projection, with the contribution of scenario uncertainty comparable to model uncertainty. Reducing model uncertainty can effectively narrow the monsoon precipitation projection. The internal variability estimates differ slightly among models and methods, the uncertainty partitioning is robust in middle‐long term. Plain Language Summary: Climate projections are subject to large uncertainty arisen from climate internal variability, model uncertainty, and scenario uncertainty. Understanding the sources of uncertainty is fundamental for narrowing the uncertainty in projections, further leading to more reliable future climate projections required for decision‐making. Focusing on the global land monsoon region, we quantified the contributions of different uncertainty components in the projections of mean and extreme precipitation. For mean precipitation, model uncertainty dominates the projection uncertainty, with a fractional contribution of ~90%, while the contribution of internal variability (scenario uncertainty) decreases (increases) with time. For extreme precipitation, the results are generally similar except that at the end of 21st century the contribution of scenario uncertainty is comparable to model uncertainty. Reductions of model uncertainty by improving model performances or employing high‐skill models can effectively narrow the uncertainty in monsoon precipitation projection. Key Points: Model uncertainty (contributing ~90%) dominates the uncertainty in monsoon mean and extreme precipitation projectionsReducing model uncertainty and employing high‐skill models can effectively narrow the uncertainty in monsoon precipitation projectionsAlthough internal variability estimates differ slightly among models and methods, the uncertainty partitioning is robust in middle‐long term [ABSTRACT FROM AUTHOR]

In this paper, we generalize the concept of "external forcing" to include any mechanism that modulates the long-term evolution of a meteorological variable but is not directly related to the internal variability of the climate system. Applying this concept, the corresponding 'external forcings' are removed from several long record datasets of oceanic and atmospheric variables at the surface in the North Atlantic. We perform a multivariate analysis in the frequency domain over both the original data fields and the new 'internal variability' fields. This multivariate analysis is based on a MultiTaper Method-Singular Value Decomposition (MTM-SVD). It is noteworthy that, after the removal of the external forcings, there is an almost perfect alignment of the main multidecadal oscillatory band (f = 0.21 cycles/decade) with all the spectra of the analysed fields. This alignment was not observed before the external forcings were removed. Particularly striking is the case of the sea level pressure (SLP), which shows a notable variation in its oscillation period despite the fact that this variable has traditionally been considered to be unaffected, at global scale, by any external forcing. The external forcing in the SLP records is very probably caused by the scarcity of the observed data during the first hundred years of the record (most evident, during the earliest decades), by the spatial distribution of those observations and, possibly, by the assimilation model employed to build those long record datasets. When we analysed the relationship between the ocean and the atmosphere using this approach, we found strong evidence of a cyclic behaviour in which oceanic conditions modulate the atmospheric variability, with a lead time of up to about 10 years. [ABSTRACT FROM AUTHOR]

The correct mathematical approach to climate change requires the knowledge of the time-dependent system's pullback/snapshot attractor (PBA). Once the governing equations and external forcing are known, the PBA can be estimated by performing an ensemble simulation (ES) of many forward time integrations differing only by their respective initialization; the resulting ensemble mean and spread are usually considered as representative of the forced and internal variability (FV and IV), respectively. In this paper the PBA of an excitable conceptual ocean model subjected to an idealized decadal time-scale aperiodic forcing is determined and is then used to show that the system's relaxation oscillations contribute substantially to the ensemble mean, despite their intrinsic nature: as a consequence, a clear separation between the FV and IV is impossible in this case study. This provides an example of dynamical behaviour which may be typical of climate ESs under fluctuating aperiodic forcing. The impact of the number of ensemble members N on the statistical significance of the ES is then investigated. The complexity of realistic climate modelling currently imposes N = O(100): how significant is the statistical information derived from such small ESs? To answer this question for the present case study, the knowledge of the PBA is exploited to carry out a systematic comparison between the latter and small ESs with N = 50, also by using novel quantifiers specifically conceived for this purpose. The results reveal a remarkable significance of such ESs beyond the predictability time and may provide useful information for the design of future realistic ESs. [ABSTRACT FROM AUTHOR]

Global temperature change is strongly affected by internal climate variability (ICV). The temporal change of the ICV on the decadal to multi‐decadal scales is referred as the decadal modulated oscillation (DMO) that plays a dominated role in the occurrence of enhanced warming and warming hiatus. However, investigation on the DMO in modern historical period has received limited attention. In this study, the ensemble empirical mode decomposition (EEMD) method was applied to the surface air temperature (SAT) during the boreal cold season to extract the DMO signal in the past century. Two most sensitive areas of DMO trend over northern Eurasia and northwestern North America were identified and used to build a time series of regionally enhanced DMO. It showed an obvious decadal periodic oscillation at 11–23 years and exhibited increasing amplitude. In addition, regression analysis using Niño3.4, Pacific Decadal Oscillation (PDO), Atlantic Multi‐decadal Oscillation (AMO), and Arctic Oscillation (AO) revealed a major role of the AO in DMO over the mid‐to‐high latitudes in the Northern Hemisphere (NH). However, such strong oscillation signal has not been detected in most of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and the extracted regionally enhanced DMO are capable of improving the predictability of SAT over the mid‐to‐high latitudes in the NH. [ABSTRACT FROM AUTHOR]

The assessment of climate change impacts is usually done by calculating the change in drought conditions between future and historical periods by using multiple climate model simulations. However, this approach usually focuses on anthropogenic climate changes (ACCs) while ignoring the internal climate variability (ICV) caused by the chaotic nature of the climate system. Recent studies have shown that ICV plays an important role in the projected future climate change. To evaluate that role, this study quantifies the contribution of ICV to climate change impacts on regional droughts by using the signal-to-noise ratio (SNR) and the fraction of standard deviation (FOSD) as metrics for China. The internal climate variability or noise (i.e. ICV) is estimated as the inter-member variability of two climate models' large-member ensembles; the signal (i.e. ACC) and the climate model uncertainty (or inter-model uncertainty, IMU) are estimated as the ensemble mean and inter-model variability of 29 global climate models, respectively. The drought conditions are characterized by drought frequency, duration and severity, which are quantified by using the theory of run based on the standardized precipitation evapotranspiration index (SPEI). The results show that deteriorated drought conditions induced by ACCs are projected to occur over China. From the perspective of the SNR, the ICV impacts are less significant compared to the ACC impacts for drought metrics. Remarkable spatial variations of SNRs for future drought metrics are found, with values varying from 0.001 to exceeding 10. In terms of the FOSD, ICV contributions relative to the IMU are large, as FOSDs are >1 for around 22% grids. These results imply the significance of taking into account the impacts of ICV in drought assessment, any study ignores the influence of ICV may be biased. Unlabelled Image • This study quantifies the contribution of internal climate variability to climate change impacts on drought. • The internal climate variability is less influential compared to anthropogenic climate change on future droughts. • The internal climate variability contributes significantly to the uncertainty envelope of drought estimations. [ABSTRACT FROM AUTHOR]

Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates. Key Points: Estimates are highly sensitive when using a single source to approximate internal variabilityProbabilistic estimates are too narrow if the internal variability comes from a single modelEstimates tend to converge when enough samples of internal variability are used [ABSTRACT FROM AUTHOR]

The Arctic region has experienced significant warming during the past two decades with major implications on the cryosphere. The causes of Arctic amplification are still an open question within the scientific community, attracting recent interest. The goal of this study is to quantify the contribution of atmospheric circulation on temperature variability in the Atlantic–Arctic region at decadal to intra‐annual timescales from 1951 to 2014. Daily 20th Century reanalyses geopotential height anomalies at 500 hPa were clustered into different weather regimes to assess their contribution to observed temperature variability. The results show that in winter, 25% of the warming (cooling) in the North Atlantic Ocean (northeastern Canada) is due to temporal decreases of high geopotential anomalies in Greenland. This regime influences air mass migration patterns, bringing less cold (warm) air masses into these regions. Additionally, atmospheric warming or cooling has been attributed to a change in nearby oceanic basin surface conditions because of sea ice decline. In summer, about 15% of the warming observed in Norwegian/Greenland Seas is related to an increase in temporal anticyclonic patterns. This ratio reaches 37% in Norway due to an amplification from downwards solar radiation. This study allows for better understanding how natural climate variability modulates the regional signature of climate change and estimating the uncertainties in climate projections. [ABSTRACT FROM AUTHOR]

Based on outputs of historical runs of 38 members from the Community Earth System Model (CESM) Large Ensemble Project, this study evaluates the capability of the model to simulate the winter climate over the Northern Hemisphere, with an emphasis on Eurasia. The climatology and interannual variability of the wintertime circulation and surface air temperature (SAT) can be well reproduced. The multi-member mean of the long-term trends of sea level pressure (SLP) and SAT resemble the observations, suggesting the capability of CESM to reproduce the forced component of the observed trends. Meanwhile, the trends of SLP and SAT show large diversity among the 38 members, implying the importance of low-frequency variability in the observed trends. Considering the high skill of the CESM to simulate the observed climate, the internal climate variability described by the inter-member spread is further examined. It reveals that the internal climate variability of wintertime SLP over Eurasia can be captured by analyzing the inter-member spread of their climatology, interannual variability, or long-term trend. Moreover, the variations of the inter-member spread are regulated by the same mechanism as that of the observed interannual variability, implying the feasibility of explaining the characteristics and mechanism of the inter-member spread with the corresponding interannual variability in observations. [ABSTRACT FROM AUTHOR]