Internal Climate Variability Research Articles

Role of the warm Arctic cold Eurasian-like pattern on the near future warming rate of East Asian surface temperature

Abstract Internal climate variability (ICV) plays an important role in either accelerating or slowing down the rate of surface temperature warming in East Asia in the near future. To examine the influence of ICV on East Asian surface temperature in the near future, we mainly analyzed the data sets obtained from Max Planck Institute Grand Ensemble model simulations under the Representative Concentration Pathway 8.5 scenario. It is found that the ICV associated with the so-called Warm Arctic-Cold Eurasian (WACE)-like pattern contributes to the near-future warming rate of East Asian surface temperature. Similar results are also obtained from large ensemble model simulations participating in the Coupled Model Intercomparison Project Phase 6 under the Shared Socioeconomic Pathways 5-8.5 scenario. This implies that the near-term warming rate in East Asia could vary depending on how the climate model simulates the WACE-like pattern, indicating that the ability to accurately simulate ICV in climate models is crucial for future climate mitigation and adaptation policies.

Seasonal amplification of subweekly temperature variability over extratropical Southern Hemisphere land masses

Temperature variability has substantial socioeconomic impacts through its association with the frequency and severity of heat extremes. Under anthropogenic influence, climate models project seasonally-dependent amplifications of near-surface temperature variability over some sectors of the Southern Hemisphere, and robust positive trends have already been observed in recent decades. Here we show that the amplification of subweekly temperature variability simulated by the multi-model ensemble mean of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) over South Africa, Australia, and South America is often substantially smaller than in reanalyses in recent decades, reaching a similar amplification only at the end of the 21st century due to a weaker amplification of subweekly variance generation efficiency. Analysis of a large model ensemble indicates that this discrepancy may be due to internal climatic variability suggesting that the recent rapid amplification seen in reanalyses may slow down or even temporarily reverse in the near future.

Changing windstorm characteristics over the US Northeast in a single model large ensemble

Abstract Extreme windstorms pose a significant hazard to infrastructure and public safety, particularly in the highly populated US Northeast (NE). However, the influence climate change and changing land use will have on these events remains unclear. A large ensemble generated using the Max-Planck Institute (MPI) Earth system model is used to generate projections of NE windstorms under different shared socioeconomic pathways (SSPs) and to attribute changes to projected land use land cover (LULC) change, externally forced changes and internal climate variability. To reduce the influence of coarse grid cell resolution and uncertainties in surface roughness lengths, windstorms are identified using simultaneous widespread exceedance of local 99th percentile 10 m wind speeds (U99). Projected declines in forest cover in the NE and the resulting reductions in surface roughness length under SSP3-7.0 lead to projections of large increases in U99 and derived windstorm intensity and scale. However, these projected changes in regional LULC under SSP3-7.0 are unprecedented in a historical context and may not be realistic. After corrections are applied to remove the influence of LULC on wind speeds, regionally averaged U99 exhibit declines for most of the single model initial-condition large ensemble (SMILE) members which are broadly proportional to the radiative forcing and global air temperature increase in the SSPs, with a median value of −0.15 ms−1 °C−1. While weak cyclones are projected to decline in frequency in the NE, intense cyclones and the resulting windstorms and indices of socioeconomic loss do not. Where present, significant trends in these loss indices are positive, and some MPI SMILE members generate future windstorms that are unprecedented in the historical period.

Impacts of forced and internal climate variability on changes in convective environments over the eastern United States

Hazards from convective weather pose a serious threat to the contiguous United States (CONUS) every year. Previous studies have examined how future projected changes in climate might impact the frequency and intensity of convective weather using simulations with both convection-permitting regional models and coarser-grid climate and Earth system models. We build on this existing literature by utilizing a large-ensemble of historical and future Earth system model simulations to investigate the time evolution of the forced responses in large-scale convective environments and how those responses might be modulated by the rich spectrum of internal climate variability. Specifically, daily data from an ensemble of 50 simulations with the most recent version of the Community Earth System Model was used to examine changes in the convective environment over the eastern CONUS during March-June from 1870 to 2100. Results indicate that anthropogenically forced changes include increases in convective available potential energy and atmospheric stability (convective inhibition) throughout this century, while tropospheric vertical wind shear is projected to decrease across much of the CONUS. Internal climate variability on decadal and longer time scales can either significantly enhance or suppress these forced changes. The time evolution of two-dimensional histograms of convective indices suggests that future springtime convective environments over the eastern CONUS may, on average, be supportive of relatively less frequent and shorter-lived, but deeper and more intense convection.

Enhanced global carbon cycle sensitivity to tropical temperature linked to internal climate variability.

The sensitivity of atmospheric CO2 growth rate to tropical temperature (γT) has almost doubled between 1959 and 2011, a trend that has been linked to increasing drought in the tropics. However, γT has declined since then. Understanding whether these variations in γT reflect forced changes or internal climate variability in the carbon cycle is crucial for future climate projections. We show that doubling sensitivity events can arise in simulations by Earth system models with perturbed initial conditions but are likely explained by internal climate variability. We show that the doubling sensitivity event is associated with the occurrence of a few, but very strong, El Niño events, such as 1982/83 and 1997/98. Such extreme events result in concurrent carbon release by tropical and extratropical ecosystems, increasing the variance of the global land carbon sink and its apparent sensitivity to tropical temperature. Our results imply that the doubling sensitivity does not necessarily indicate a change in carbon cycle response to climate change.

Accelerated warming of High Mountain Asia predicted at multiple years ahead

High-Mountain Asia (HMA) is an important source of freshwater since it holds the largest reservoir of frozen water outside the polar regions. HMA feeds ten great rivers, ultimately supporting more than 2 billion people. However, the threat of accelerated glacier melt, which is a consequence of unprecedented global warming since the early 1950s, threatens water resources in the surrounding countries. Accurate predictions of the near-term temperature change and synergistic mass loss of glaciers are essential but challenging to implement because of the impacts of internal climate variability. Here, based on large ensembles of state-of-the-art decadal climate prediction experiments, we provide evidence that the internally generated surface air temperature variations in HMA can be predicted multiple years in advance, and the model initialization has robust added value to the decadal prediction skill. Real-time decadal forecasts suggest that the HMA will experience accelerated warming in 2025–2032, where the surface warming will increase by 0.98 °C (0.67 to 1.33 °C; 5 % to 95 % range) relative to the reference period 1991–2020, which is equivalent to 1.75 times the observed warming during 2016–2023. The decadal predictability originates from extratropical Pacific decadal variability modes, which modulate the convective heating in the tropical Pacific and influence HMA via the eastward-propagating atmospheric Kelvin waves. Accelerated warming in the coming decade will likely increase the shrinkage of the glacier volume over the HMA by 1.4 %. This change poses unprecedented challenges, including potential water scarcity, ecosystem disruption, and increased risk of natural disasters, to HMA and surrounding regions.

Higher-order internal modes of variability imprinted in year-to-year California streamflow changes

Climate internal variability plays a crucial role in the hydroclimate system, and this study quantifies its predictability on streamflow in California using historical observations, climate simulations, and various machine learning (ML) models. Here we demonstrate that while 5% of the year-to-year variability in seasonal peak streamflow can be attributed to the well-known climate variability indices, the explained variance surpasses 30% when higher-order empirical orthogonal functions of these indices are retained in the analysis. Notably, the results highlight the significant influence of the 5th empirical mode of the Pacific North American pattern and of the Pacific Decadal Oscillation in shaping the streamflow variability, which is consistent across all the tested ML models. A deeper investigation reveals a clear and monotonic quasi-linear response of streamflow to these dominant patterns, emphasizing the substantial role played by higher-order internal modes of variability in shaping regional hydroclimate systems, which contributes to bridging the gap between the well-known variability domains and local climate systems.

Large-ensemble Monte Carlo: a researcher’s guide to better climate trend uncertainties

Abstract 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.

Anthropogenic Fingerprint Detectable in Upper Tropospheric Ozone Trends Retrieved from Satellite.

Tropospheric ozone (O3) is a strong greenhouse gas, particularly in the upper troposphere (UT). Limited observations point to a continuous increase in UT O3 in recent decades, but the attribution of UT O3 changes is complicated by large internal climate variability. We show that the anthropogenic signal ("fingerprint") in the patterns of UT O3 increases is distinguishable from the background noise of internal variability. The time-invariant fingerprint of human-caused UT O3 changes is derived from a 16-member initial-condition ensemble performed with a chemistry-climate model (CESM2-WACCM6). The fingerprint is largest between 30°S and 40°N, especially near 30°N. In contrast, the noise pattern in UT O3 is mainly associated with the El Niño-Southern Oscillation (ENSO). The UT O3 fingerprint pattern can be discerned with high confidence within only 13 years of the 2005 start of the OMI/MLS satellite record. Unlike the UT O3 fingerprint, the lower tropospheric (LT) O3 fingerprint varies significantly over time and space in response to large-scale changes in anthropogenic precursor emissions, with the highest signal-to-noise ratios near 40°N in Asia and Europe. Our analysis reveals a significant human effect on Earth's atmospheric chemistry in the UT and indicates promise for identifying fingerprints of specific sources of ozone precursors.

Internally Driven Variability of the Angola Low is the Main Source of Uncertainty for the Future Changes in Southern African Precipitation

AbstractVariations in southern African precipitation have a major impact on local communities, increasing climate‐related risks and affecting water and food security, as well as natural ecosystems. However, future changes in southern African precipitation are uncertain, with climate models showing a wide range of responses from near‐term projections (2020–2040) to the end of the 21st century (2080–2100). Here, we assess the uncertainty in southern African precipitation change using five Ocean‐Atmosphere General Circulation single model initial‐condition large ensembles (30–50 ensemble members) and four emissions scenarios. We show that the main source of uncertainty in 21st Century projections of southern African precipitation is the internal climate variability. In addition, we find that differences between ensemble members in simulating future changes in the location of the Angola Low explain a large proportion (∼60%) of the uncertainty in precipitation change. Together, the internal variations in the large‐scale circulation over the Pacific Ocean and the Angola Low explain ∼64% of the uncertainty in southern African precipitation change. We suggest that a better understanding of the future evolutions of the southern African precipitation may be achieved by understanding better the model's ability to simulate the Angola Low and its effects on precipitation.

Observation-inferred resilience loss of the Amazon rainforest possibly due to internal climate variability

Abstract. Recent observation-based studies suggest that the Amazon rainforest has lost substantial resilience since 1990, indicating that the forest might undergo a critical transition in the near future due to global warming and deforestation. The idea is to use trends in a lag-1 auto-correlation of leaf density as an early-warning signal of an imminent critical threshold for rainforest dieback. Here we test whether the observed change in auto-correlations could arise from internal variability using historical and control simulations of nine sixth-generation Earth system model ensembles (Phase 6 of the Coupled Model Intercomparison Project, CMIP6). We quantify trends in the leaf area index auto-correlation from both models and satellite-observed vegetation optical depth from 1990 to 2017. Four models reproduce the observed trend with at least one historical realization whereby the observations lie at the upper limit of model variability. Three out of these four models exhibit similar behavior in control runs, suggesting that historical forcing is not necessary for simulating the observed trends. Furthermore, we do not observe a critical transition in any future runs under the strongest greenhouse gas emission scenario (SSP5-8.5) until 2100 in the four models that best reproduce the past observed trends. Hence, the currently observed trends could be caused simply by internal variability and, unless the data records are extended, have limited applicability as an early-warning signal. Our results suggest that the current rapid decline in the Amazon rainforest coverage is not foremost caused by global warming.

Land-Use Feedback under Global Warming—A Transition from Radiative to Hydrological Feedback Regime

Abstract This study examines the effects of land-use (LU) change on regional climate, comparing historical and future scenarios using seven climate models from phase 6 of Coupled Model Intercomparison Project–Land Use Model Intercomparison Project experiments. LU changes are evaluated relative to land-use conditions during the preindustrial climate. Using the Community Earth System Model, version 2–Large Ensemble (CESM2-LE) experiment, we distinguish LU impacts from natural climate variability. We assess LU impact locally by comparing the impacts of climate change in neighboring areas with and without LU changes. Further, we conduct CESM2 experiments with and without LU changes to investigate LU-related climate processes. A multimodel analysis reveals a shift in LU-induced climate impacts, from cooling in the past to warming in the future climate across midlatitude regions. For instance, in North America, LU’s effect on air temperature changes from −0.24° ± 0.18°C historically to 0.62° ± 0.27°C in the future during the boreal summer. The CESM2-LE shows a decrease in LU-driven cooling from −0.92° ± 0.09°C in the past to −0.09° ± 0.09°C in future boreal summers in North America. A hydroclimatic perspective linking LU and climate feedback indicates LU changes causing soil moisture drying in the midlatitude regions. This contrasts with hydrology-only views showing wetter soil conditions due to LU changes. Furthermore, global warming causes widespread drying of soil moisture across various regions. Midlatitude regions shift from a historically wet regime to a water-limited transitional regime in the future climate. This results in reduced evapotranspiration, weakening LU-driven cooling in future climate projections. A strong linear relationship exists between soil moisture and evaporative fraction in midlatitudes. Significance Statement Land–atmosphere feedback involving soil moisture can increase local temperature and affect how land-use (LU) change impacts manifest in a warming climate. Conversely, an increased surface reflectance due to LU change can decrease local temperature in the midlatitude regions. Further, the LU change signal is often mixed with the internal climate variability, making it harder to separate. This study uses a novel technique to separate LU change impact from other climate forcing in the latest generation of climate and Earth system models. In the future climate, soil moisture drying lessens the cooling impact. A large-ensemble climate experiment analysis confirms a significant weakening of the LU-driven cooling impact in the midlatitudes. Both LU and climate changes exacerbate soil moisture drying, leading to a shift toward a water-limited system where hydrological feedback becomes more influential than radiative feedback.

Anthropogenic Changes in Interannual-to-Decadal Climate Variability in CMIP6 Multiensemble Simulations

Abstract As well as having an impact on the background state of the climate, global warming due to human activities could affect its natural oscillations and internal variability. In this study, we use four initial-condition ensembles from the CMIP6 framework to investigate the potential evolution of internal climate variability under different warming pathways for the twenty-first century. Our results suggest significant changes in natural climate variability and point to two distinct regimes driving these changes. The first is a decrease in internal variability of surface air temperature at high latitudes and all frequencies, associated with a poleward shift and the gradual disappearance of sea ice edges, which we show to be an important component of internal variability. The second is an intensification of the interannual variability of surface air temperature and precipitation at low latitudes, which appears to be associated with El Niño–Southern Oscillation (ENSO). This second regime is particularly alarming because it may contribute to making the climate more unstable and less predictable, with a significant impact on human societies and ecosystems.

Can climate change signals be detected from the terrestrial water storage at daily timescale?

The global terrestrial water storage (TWS), the most accessible component in the hydrological cycle, is a general indicator of freshwater availability on Earth. The global TWS trend caused by climate change is harder to detect than global mean temperature due to the highly uneven hydrological responses across the globe, the brevity of global freshwater observations, and large noises of internal climate variability. To overcome the climate noise and small sample size of observations, we leverage the vast amount of observed and simulated meteorological fields at daily scales to project global TWS through its fingerprints in weather patterns. The novel method identifies the relationship between annual global mean TWS and daily surface air temperature and humidity fields using multi-model hydrological simulations. We found that globally, approximately 50% of days for most years since 2016 have climate change signals emerged above the noise of internal variability. Climate change signals in global mean TWS have been consistently increasing over the last few decades, and in the future, are expected to emerge from the natural climate variability. Our research indicates the urgency to limit carbon emission to not only avoid risks associated with warming but also sustain water security in the future.

Asian summer monsoon responses under RCP4.5 and RCP8.5 scenarios in CESM large ensemble simulations

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.

Improving the forecast quality of near-term climate projections by constraining internal variability based on decadal predictions and observations

Projections of near-term climate change in the next few decades are subject to substantial uncertainty from internal climate variability. Approaches to reduce this uncertainty by constraining the phasing of climate variability based on large ensembles of climate simulations have recently been developed. These approaches select those ensemble members that are in closer agreement with sea surface temperature patterns from either observations or initialized decadal predictions. Previous studies demonstrated the benefits of these constraints for projections up to 20 years into the future, but these studies applied the constraints to different ensembles of climate simulations, which prevents a consistent comparison of methods or identification of specific advantages of one method over another. Here we apply several methods to constrain internal variability phases, using either observations or decadal predictions as constraining reference, to an identical multi-model ensemble consisting of 311 simulations from 37 models from the Coupled Model Intercomparison Project phase 6 (CMIP6), and compare their forecast qualities. We show that constraining based on both observations and decadal predictions significantly enhances the skill of 10 and 20-year projections for near-surface temperatures in some regions, and that constraining based on decadal predictions leads to the largest added value in terms of probabilistic skill. We further explore the sensitivity to different implementations of the constraint that focus on the patterns of either internal variability alone or a combination of internal variability and long-term changes in response to forcing. Looking into the near-term future, all variations of the constraints suggest an accelerated warming of large parts of the Northern Hemisphere for the period 2020–2039, in comparison to the unconstrained CMIP6 ensemble.

The regionality and seasonality of tornado trends in the United States

Continued efforts to build human resilience to the impacts of tornadoes require updated knowledge of tornado occurrences as well as how their occurrence characteristics may be changing in time and varying by region. We have temporally and geospatially disaggregated annual tornado reports in the United States and revealed that significant, long-term decreases in tornado days from 1960 to 2022 have occurred over the months of June through August, primarily within the Southern Great Plains. In contrast, long-term increases in days of tornado outbreaks have occurred over this period, particularly within the Southeast U.S. and during warm- as well as cool-season months. There are indications that these dichotomous linear trends in tornado days and tornado outbreaks have relaxed over the most recent decade. Our study highlights the need to better understand the role of internal climate variability and anthropogenic forcing in modulating tornado activity.

Historical sensible-heat-flux variations key to predicting future hydrologic sensitivity

Under anthropogenic climate change (CC), the global hydrological cycle intensifies at a rate known as hydrologic sensitivity (HS). Global climate models (GCMs) exhibit substantial uncertainty in HS. Past work suggests that another form of HS, derived from internal climate variability (IV), is useful for constraining this uncertainty. However, these two forms of HS are weakly related. Here we show that decomposing HS under both CC and IV, based on the global energy budget, provides insight into the likely range of future HS. We find that sensible heat exchange between the atmosphere and ocean is not accounted for in the atmospheric energy budget under IV, masking the connection between HS under IV and CC. Removing this term, a closer relationship emerges. We use observations in conjunction with this relationship to suggest an upward shift in the likely range of future HS (66% confidence interval: 2.00–2.36 W m−2 K−1).

Extreme Tibetan Plateau cooling caused by tropical volcanism

The extreme cooling of the Tibetan Plateau (TP) during the boreal winter typically poses threats to the local environment and people’s safety, and it is usually attributed to internal climate variability. Here we demonstrate that the five recent large tropical volcanic eruptions since 1880 have caused an average extreme cooling of up to −0.80 K on the TP in observations during the first boreal winter following the eruptions. This cooling effect is much larger than the global average terrestrial cooling of −0.30 K after the eruptions. The multi-model ensemble mean (MME) of the Atmospheric Model Intercomparison Project (AMIP) runs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6), in which realistic sea surface temperatures (SST) were specified, can simulate an extreme TP cooling response of up to −0.79 K, which is much larger than the direct aerosol cooling of −0.36 K simulated by the historical runs. The positive North Atlantic Oscillation (NAO) anomaly during the post-eruption winter after the eruptions plays a key role in amplifying the TP cooling through atmospheric teleconnection, which overwhelms the warming response associated with the frequently occurring El Niños. The results from this study provide a perspective on the potential contribution of volcanic activity or stratospheric sulfur injection scenarios to specific TP cooling.

Quantifying the Impact of Internal Variability on the CESM2 Control Algorithm for Stratospheric Aerosol Injection

AbstractEarth system models are powerful tools to simulate the climate response to hypothetical climate intervention strategies, such as stratospheric aerosol injection (SAI). Recent simulations of SAI implement a tool from control theory, called a controller, to determine the quantity of aerosol to inject into the stratosphere to reach or maintain specified global temperature targets, such as limiting global warming to 1.5°C above pre‐industrial temperatures. This work explores how internal (unforced) climate variability can impact controller‐determined injection amounts using the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE‐SAI) simulations. Since the ARISE‐SAI controller determines injection amounts by comparing global annual‐mean surface temperature to predetermined temperature targets, internal variability that impacts temperature can impact the total injection amount as well. Using an offline version of the ARISE‐SAI controller and data from Earth system model simulations, we quantify how internal climate variability and volcanic eruptions impact injection amounts. While idealized, this approach allows for the investigation of a large variety of climate states without additional simulations and can be used to attribute controller sensitivities to specific modes of internal variability.