scholarly journals Forced changes in internal variability: an additional uncertainty to deal with

2021 ◽  
Author(s):  
Daniel Topal ◽  
Tímea Haszpra ◽  
Mátyás Herein
Keyword(s):  
Time Series ◽  
Radiative Forcing ◽  
Large Scale ◽  
Chaotic Behavior ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Climate System ◽  
Initial Condition ◽  
Coupled Modes ◽  
Anthropogenic Forcing

<p>Anthropogenic activities contribute to the rising level of greenhouse gas concentrations in the atmosphere at a rate of approximately 1% per year providing a time-dependent external radiative forcing on the climate. In addition to tangible consequences of anthropogenic forcing affecting the climate system, simultaneous, less apparent changes occurring on low-frequency timescales demand effort to deal with. These include changes in natural internal processes of the climate system due to the non-stationary anthropogenic forcing. This represents additional uncertainty affecting future model projections on top of internal variability, scenario and model uncertainty. Here, with the application of state-of-the-art Single Model Initial-condition Large Ensemble (SMILE) simulations – that account for the chaotic behavior of the climate system with perturbed initial condition runs of the same model – we offer a way forward for new perspectives on externally-forced changes in internal variability. In doing so, we utilize an approach for analyzing SMILEs called the <em>snapshot view</em>, which offers a mathematically exact and elegant formulation and the potential to complement previous, time-series-based diagnostics with ensemble-based statistics. We reveal how the <em>snapshot view</em> allows for surprisingly simple practices to detect anthropogenically forced changes in modes of large-scale internal atmospheric circulation variability (so-called “teleconnection patterns”) as well as coupled modes of atmospheric variability with Arctic sea ice. A crucial message of the <em>snapshot view</em> is that all of the traditional, time series-based methods can be reformulated for ensembles and thus, on the one hand, ambiguous results arising from subjective choices of traditional methods (e.g. length and center of time windows) can be avoided, and on the other hand, new perspectives open for detecting forced changes in internal variability.</p>

scholarly journals Internal Variability in Projections of Twenty-First-Century Arctic Sea Ice Loss: Role of the Large-Scale Atmospheric Circulation

2014 ◽  
Vol 27 (2) ◽  
pp. 527-550 ◽  
Author(s):  
Justin J. Wettstein ◽  
Clara Deser
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Wave Train ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
Rossby Wave Train ◽  
Arctic Sea Ice Loss

Abstract Internal variability in twenty-first-century summer Arctic sea ice loss and its relationship to the large-scale atmospheric circulation is investigated in a 39-member Community Climate System Model, version 3 (CCSM3) ensemble for the period 2000–61. Each member is subject to an identical greenhouse gas emissions scenario and differs only in the atmospheric model component's initial condition. September Arctic sea ice extent trends during 2020–59 range from −2.0 × 106 to −5.7 × 106 km2 across the 39 ensemble members, indicating a substantial role for internal variability in future Arctic sea ice loss projections. A similar nearly threefold range (from −7.0 × 103 to −19 × 103 km3) is found for summer sea ice volume trends. Higher rates of summer Arctic sea ice loss in CCSM3 are associated with enhanced transpolar drift and Fram Strait ice export driven by surface wind and sea level pressure patterns. Over the Arctic, the covarying atmospheric circulation patterns resemble the so-called Arctic dipole, with maximum amplitude between April and July. Outside the Arctic, an atmospheric Rossby wave train over the Pacific sector is associated with internal ice loss variability. Interannual covariability patterns between sea ice and atmospheric circulation are similar to those based on trends, suggesting that similar processes govern internal variability over a broad range of time scales. Interannual patterns of CCSM3 ice–atmosphere covariability compare well with those in nature and in the newer CCSM4 version of the model, lending confidence to the results. Atmospheric teleconnection patterns in CCSM3 suggest that the tropical Pacific modulates Arctic sea ice variability via the aforementioned Rossby wave train. Large ensembles with other coupled models are needed to corroborate these CCSM3-based findings.

Chaos in Estimates of Climate Change Impacts

2020 ◽  
Author(s):  
Emanuele Massetti ◽  
Emanuele Di Lorenzo
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Initial Conditions ◽  
Climate Change Impacts ◽  
Internal Variability ◽  
Future Climate ◽  
Climate System ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Exogenous Forcing

<p>Estimates of physical, social and economic impacts of climate change are less accurate than usually thought because the impacts literature has largely neglected the internal variability of the climate system. Climate change scenarios are highly sensitive to the initial conditions of the climate system due the chaotic dynamics of weather. As the initial conditions of the climate system are unknown with a sufficiently high level of precision, each future climate scenario – for any given model parameterization and level of exogenous forcing – is only one of the many possible future realizations of climate. The impacts literature usually relies on only one realization randomly taken out of the full distribution of future climates. Here we use one of the few available large scale ensembles produced to study internal variability and an econometric model of climate change impacts on United States (US) agricultural productivity to show that the range of impacts is much larger than previously thought. Different ensemble members lead to significantly different impacts. Significant sign reversals are frequent. Relying only on one ensemble member leads to incorrect conclusions on the effect of climate change on agriculture in most of the US counties. Impacts studies should start using large scale ensembles of future climate change to predict damages. Climatologists should ramp-up efforts to run large ensembles for all GCMs, for at least the most frequently used scenarios of exogenous forcing.</p>

scholarly journals The long-term variability of extreme sea levels in the German Bight

Ocean Science ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 651-668 ◽  
Author(s):  
Andreas Lang ◽  
Uwe Mikolajewicz
Keyword(s):  
Sea Level ◽  
German Bight ◽  
Large Scale ◽  
Internal Variability ◽  
Climate System ◽  
System Model ◽  
Tide Gauge Record ◽  
Sea Levels ◽  
The North

Abstract. Extreme high sea levels (ESLs) caused by storm floods constitute a major hazard for coastal regions. We here quantify their long-term variability in the southern German Bight using simulations covering the last 1000 years. To this end, global earth system model simulations from the PMIP3 past1000 project are dynamically scaled down with a regionally coupled climate system model focusing on the North Sea. This approach provides an unprecedented long high-resolution data record that can extend the knowledge of ESL variability based on observations, and allows for the identification of associated large-scale forcing mechanisms in the climate system. While the statistics of simulated ESLs compare well with observations from the tide gauge record at Cuxhaven, we find that simulated ESLs show large variations on interannual to centennial timescales without preferred oscillation periods. As a result of this high internal variability, ESL variations appear to a large extent decoupled from those of the background sea level, and mask any potential signals from solar or volcanic forcing. Comparison with large-scale climate variability shows that periods of high ESL are associated with a sea level pressure dipole between northeastern Scandinavia and the Gulf of Biscay. While this large-scale circulation regime applies to enhanced ESL in the wider region, it differs from the North Atlantic Oscillation pattern that has often been linked to periods of elevated background sea level. The high internal variability with large multidecadal to centennial variations emphasizes the inherent uncertainties related to traditional extreme value estimates based on short data subsets, which fail to account for such long-term variations. We conclude that ESL variations as well as existing estimates of future changes are likely to be dominated by internal variability rather than climate change signals. Thus, larger ensemble simulations will be required to assess future flood risks.

Application of linear dynamical mode decomposition to ensembles of climate simulations

2021 ◽  
Author(s):  
Maria Buyanova ◽  
Sergey Kravtsov ◽  
Andrey Gavrilov ◽  
Dmitry Mukhin ◽  
Evgeny Loskutov ◽  
...  
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
20Th Century ◽  
Time Scales ◽  
Principal Components ◽  
Internal Variability ◽  
Forced Response ◽  
Climate System ◽  
External Forcing ◽  
Function Decomposition

<p>An analysis of the climate system is usually complicated by its very high dimensionality and its nonlinearity which impedes spatial and time scale separation. An even more difficult problem is to obtain separate estimates of the climate system’s response to external forcing (e.g. anthropogenic emissions of greenhouse gases and aerosols) and the contribution of the climate system’s internal variability into recent climate trends. Identification of spatiotemporal climatic patterns representing forced signals and internal variability in global climate models (GCMs) would make it possible to characterize these patterns in the observed data and to analyze dynamical relationships between these two types of climate variability.</p><p>In contrast with real climate observations, many GCMs are able to provide ensembles of many climate realizations under the same external forcing, with relatively independent initial conditions (e.g. LENS [1], MPI-GE [2], CMIP ensembles of 20th century climate). In this report, a recently developed method of empirical spatio-temporal data decomposition into linear dynamical modes (LDMs) [3] based on Bayesian approach, is modified to address the problem of self-consistent separation of the climate system internal variability modes and the forced response signals in such ensembles. The LDM method provides the time series of principal components and corresponding spatial patterns; in application to an ensemble of realizations, it determines both time series of the internal variability modes of current realization and the time series of forced response (defined as signal shared by all realizations). The advantage of LDMs is the ability to take into account the time scales of the system evolution better than some other linear techniques, e.g. traditional empirical orthogonal function decomposition. Furthermore, the modified ensemble LDM (E-LDM) method is designed to determine the optimal number of principal components and to distinguish their time scales for both internal variability modes and forced response signals.</p><p>The technique and results of applying LDM method to different GCM ensemble realizations will be presented and discussed. This research was supported by the Russian Science Foundation (Grant No. 18-12-00231).</p><p>[1] Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J., Bates, S., Danabasoglu, G., Edwards, J., Holland, M. Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A., Munoz, E., Neale, R., Oleson, K., Polvani, L., and M. Vertenstein (2015), The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-13-00255.1, 96, 1333-1349 </p><p>[2] Maher, N., Milinski, S., Suarez-Gutierrez, L., Botzet, M., Dobrynin, M., Kornblueh, L., Kröger, J., Takano, Y., Ghosh, R., Hedemann, C., Li, C., Li, H., Manzini, E., Notz, N., Putrasahan, D., Boysen, L., Claussen, M., Ilyina, T., Olonscheck, D., Raddatz, T., Stevens, B. and Marotzke, J. (2019). The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability. Journal of Advances in Modeling Earth Systems, 11, 1-21. https://doi.org/10.1029/2019MS001639</p><p>[3] Gavrilov, A., Kravtsov, S., Mukhin, D. (2020). Analysis of 20th century surface air temperature using linear dynamical modes. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(12), 123110. https://doi.org/10.1063/5.0028246</p>

scholarly journals Comparison of simulated and reconstructed variations in East African hydroclimate over the last millennium

2016 ◽  
Vol 12 (7) ◽  
pp. 1499-1518 ◽  
Author(s):  
François Klein ◽  
Hugues Goosse ◽  
Nicholas E. Graham ◽  
Dirk Verschuren
Keyword(s):  
Time Series ◽  
Indian Ocean ◽  
East Africa ◽  
Seasonal Cycle ◽  
General Circulation ◽  
Large Scale ◽  
Internal Variability ◽  
General Circulation Models ◽  
Last Millennium ◽  
East African

Abstract. The multi-decadal to centennial hydroclimate changes in East Africa over the last millennium are studied by comparing the results of forced transient simulations by six general circulation models (GCMs) with published hydroclimate reconstructions from four lakes: Challa and Naivasha in equatorial East Africa, and Masoko and Malawi in southeastern inter-tropical Africa. All GCMs simulate fairly well the unimodal seasonal cycle of precipitation in the Masoko–Malawi region, while the bimodal seasonal cycle characterizing the Challa–Naivasha region is generally less well captured by most models. Model results and lake-based hydroclimate reconstructions display very different temporal patterns over the last millennium. Additionally, there is no common signal among the model time series, at least until 1850. This suggests that simulated hydroclimate fluctuations are mostly driven by internal variability rather than by common external forcing. After 1850, half of the models simulate a relatively clear response to forcing, but this response is different between the models. Overall, the link between precipitation and tropical sea surface temperatures (SSTs) over the pre-industrial portion of the last millennium is stronger and more robust for the Challa–Naivasha region than for the Masoko–Malawi region. At the inter-annual timescale, last-millennium Challa–Naivasha precipitation is positively (negatively) correlated with western (eastern) Indian Ocean SST, while the influence of the Pacific Ocean appears weak and unclear. Although most often not significant, the same pattern of correlations between East African rainfall and the Indian Ocean SST is still visible when using the last-millennium time series smoothed to highlight centennial variability, but only in fixed-forcing simulations. This means that, at the centennial timescale, the effect of (natural) climate forcing can mask the imprint of internal climate variability in large-scale teleconnections.

scholarly journals A lower and more constrained estimate of climate sensitivity using updated observations and detailed radiative forcing time series

2014 ◽  
Vol 5 (1) ◽  
pp. 139-175 ◽  
Author(s):  
R. B. Skeie ◽  
T. Berntsen ◽  
M. Aldrin ◽  
M. Holden ◽  
G. Myhre
Keyword(s):  
Time Series ◽  
Temperature Change ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Internal Variability ◽  
Posterior Mean ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Decadal Scale ◽  
Global Mean

Abstract. Equilibrium climate sensitivity (ECS) is constrained based on observed near-surface temperature change, changes in ocean heat content (OHC) and detailed radiative forcing (RF) time series from pre-industrial times to 2010 for all main anthropogenic and natural forcing mechanism. The RF time series are linked to the observations of OHC and temperature change through an energy balance model (EBM) and a stochastic model, using a Bayesian approach to estimate the ECS and other unknown parameters from the data. For the net anthropogenic RF the posterior mean in 2010 is 2.0 Wm−2, with a 90% credible interval (C.I.) of 1.3 to 2.8 Wm−2, excluding present-day total aerosol effects (direct + indirect) stronger than −1.7 Wm−2. The posterior mean of the ECS is 1.8 °C, with 90% C.I. ranging from 0.9 to 3.2 °C, which is tighter than most previously published estimates. We find that using three OHC data sets simultaneously and data for global mean temperature and OHC up to 2010 substantially narrows the range in ECS compared to using less updated data and only one OHC data set. Using only one OHC set and data up to 2000 can produce comparable results as previously published estimates using observations in the 20th century, including the heavy tail in the probability function. The analyses show a significant contribution of internal variability on a multi-decadal scale to the global mean temperature change. If we do not explicitly account for long-term internal variability, the 90% C.I. is 40% narrower than in the main analysis and the mean ECS becomes slightly lower, which demonstrates that the uncertainty in ECS may be severely underestimated if the method is too simple. In addition to the uncertainties represented through the estimated probability density functions, there may be uncertainties due to limitations in the treatment of the temporal development in RF and structural uncertainties in the EBM.

scholarly journals The Moist Adiabat, Key of the Climate Response to Anthropogenic Forcing

Climate ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 45 ◽  
Author(s):  
Jean-Louis Pinault
Keyword(s):  
Surface Temperature ◽  
Radiative Forcing ◽  
Temperature Response ◽  
Heat Fluxes ◽  
Climate System ◽  
The Arctic ◽  
Longitudinal Distribution ◽  
Absorption Bands ◽  
Anthropogenic Forcing ◽  
Heat Exchanges

A straightforward mechanism based on properties of the moist adiabat is proposed to construe the observed latitudinal and longitudinal distribution of the anthropogenic forcing efficiency. Considering precipitation patterns at the planetary scale, idealized environmental adiabats leading to low-pressure systems are deduced. When the climate system responds to a small perturbation, which reflects radiative forcing that follows increasing anthropogenic emissions, the dry and moist adiabatic lapse rates move away from each other as the temperature of the moist adiabat at the altitude z = 0 increases. When the atmosphere becomes unstable, under the influence of the perturbation, a positive feedback loop occurs because of a transient change in the emission level height of outgoing longwave radiation in the saturated absorption bands of water vapor. During these periods of instability, the perturbation of the climate system is exerted with the concomitant warming of the surface temperature. In contrast, the return of the surface temperature to its initial value before the development of the cyclonic system is very slow because heat exchanges are mainly ruled by latent and sensible heat fluxes. Consequently, the mean surface temperature turns out to result from successive events with asymmetrical surface–atmosphere heat exchanges. The forcing efficiency differs according to whether atmospheric instability has a continental or oceanic origin. Hence the rendition of the latitudinal and longitudinal distribution of the observed surface temperature response to anthropogenic forcing, which specifies in detail the mechanisms involved in the various climate systems, including the Arctic amplification.

scholarly journals Role of internal variability of climate system in increase of air temperature in Wrocław (Poland) in the years 1951–2018

2021 ◽  
Author(s):  
Andrzej Antoni Marsz ◽  
Anna Styszyńska ◽  
Krystyna Bryś ◽  
Tadeusz Bryś
Keyword(s):  
Air Temperature ◽  
Thermal Regime ◽  
Radiative Forcing ◽  
Sunshine Duration ◽  
Rapid Change ◽  
Internal Variability ◽  
Radical Change ◽  
Climate System ◽  
Positive Trend

Abstract In the course of the annual air temperature in Wrocław (TWr variable) a rapid change of the thermal regime was found between 1987 and 1989. A similar temperature change has occurred in Central Europe. TWr increased by more than 1 deg a strong, statistically significant positive trend emerged. The analysis of processes showed that strong warming in the cold season of the year (December–March) occurred as a result of an increase in the NAO intensity and warming in the warm season as a result of increased sunshine duration. Multiple regression analysis has showed that the winter NAO Hurrell’s index explains 15% of TWr variance, and the sunshine duration of the ‘long day’ (April–August) period 49%, whereas radiative forcing 5.9%. This indicates that the factors incidental to the internal variability of the climate system explain 64% of the TWr variability and the effect of increased CO 2 concentration only ~6%. The reason for this rapid change of the thermal regime was a radical change in macro-circulation conditions in the Atlantic-European circular sector, which took place between 1988 and 1989. It has similarly changed the structure of the Central European weathers. The heat, which is the cause of warming in Wrocław, comes from an increase in solar energy inflow (April–August) and also is transported to Europe from the North Atlantic surface by atmospheric circulation (NAO). These results indicate that the role of CO 2 in shaping the contemporary temperature increase is overestimated, whereas internal variability of the climate system is underestimated.

scholarly journals Investigating the Influence of Anthropogenic Forcing on Observed Mean and Extreme Sea Level Pressure Trends over the Mediterranean Region

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Armineh Barkhordarian
Keyword(s):  
Sea Level ◽  
Mediterranean Region ◽  
Large Scale ◽  
Internal Variability ◽  
Small Scale ◽  
Sea Level Pressure ◽  
Anthropogenic Forcing ◽  
Level Pressure ◽  
Internal Climate Variability ◽  
The Mediterranean

We investigate whether the observed mean sea level pressure (SLP) trends over the Mediterranean region in the period from 1975 to 2004 are significantly consistent with what 17 models projected as response of SLP to anthropogenic forcing (greenhouse gases and sulphate aerosols, GS). Obtained results indicate that the observed trends in mean SLP cannot be explained by natural (internal) variability. Externally forced changes are detectable in all seasons, except spring. The large-scale component (spatial mean) of the GS signal is detectable in all the 17 models in winter and in 12 of the 17 models in summer. However, the small-scale component (spatial anomalies about the spatial mean) of GS signal is only detectable in winter within 11 of the 17 models. We also show that GS signal has a detectable influence on observed decreasing (increasing) tendency in the frequencies of extremely low (high) SLP days in winter and that these changes cannot be explained by internal climate variability. While the detection of GS forcing is robust in winter and summer, there are striking inconsistencies in autumn, where analysis points to the presence of an external forcing, which is not GS forcing.

scholarly journals Role of Internal Variability of Climate System in Increase of Air Temperature in Wrocław (Poland) in the Years 1951–2018

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Andrzej A. Marsz ◽  
Anna Styszyńska ◽  
Krystyna Bryś ◽  
Tadeusz Bryś
Keyword(s):  
Air Temperature ◽  
Thermal Regime ◽  
Radiative Forcing ◽  
Sunshine Duration ◽  
Rapid Change ◽  
Internal Variability ◽  
Radical Change ◽  
Climate System ◽  
Positive Trend

Abstract In the course of analysing the annual air temperature in Wrocław (TWr), a rapid change of the thermal regime was found between 1987 and 1989. TWr increased by >1°C, a strong, statistically significant positive trend emerged. The analysis of processes showed that strong warming in the cold season of the year (December–March) occurred as a result of an increase in the NAO intensity and warming in the warm season because of increased sunshine duration in Wrocław (ShWr). Multiple regression analysis has shown that the winter NAO Hurrell's index explains 15% of TWr variance, and the ShWr of the long-day (April–August) period 49%, whereas radiative forcing 5.9%. This indicates that the factors incidental to the internal variability of the climate system explain 64% of the TWr variability and the effect of increased CO2 concentration only ~6%. The reason for this rapid change of the thermal regime was a radical change in macro-circulation conditions in the Atlantic-European circular sector, which took place between 1988 and 1989. The heat, which is the cause of warming in Wrocław, comes from an increase in solar energy inflow (April–August) and also is transported to Europe from the North Atlantic surface by atmospheric circulation (NAO). These results indicate that the role of CO2 in shaping the contemporary temperature increase is overestimated, whereas the internal variability of the climate system is underestimated.

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