Inherent Uncertainty Disguises Attribution of Reduced Atmospheric CO2 Growth to Mitigation for up to a Decade

2020 ◽  
Author(s):  
Aaron Spring ◽  
Tatiana Ilyina ◽  
Jochem Marotzke
Keyword(s):  
Climate Variability ◽  
Internal Variability ◽  
Climate Mitigation ◽  
Mitigation Scenario ◽  
Growth Trend ◽  
Emission Reductions ◽  
Internal Climate Variability ◽  
Policy Shift ◽  
Near Term ◽  
Ocean Carbon

<p>On inter-annual time scales the growth rate of atmospheric CO<sub>2</sub> is largely driven by the response of the land and ocean carbon sinks to climate variability. Therefore, climate mitigation in terms of emission reductions can be disguised by internal variability.<br>However, the probability that emission reductions induced by a policy change caused reductions in atmospheric CO<sub>2</sub> growth trend is unclear. <br>We use 100 historical MPI-ESM simulations and interpret mitigation in 2020 as a policy shift from Representative Concentration Pathway 4.5 to 2.5 in a comprehensive causation attribution framework.<br>Here we show that five-year CO<sub>2</sub> trends are higher in 2021-2025 than over 2016-2020 in 30% of all realizations in the mitigation scenario, compared to 52% in the non-mitigation scenario. Therefore, mitigation is sufficient or necessary to cause these trends by 42% or 31%, respectively and therefore far from certain. <br>A stronger increase in atmospheric CO<sub>2</sub> trends despite emission reductions is possible when the global carbon cycle triggered by internal climate variability releases more CO<sub>2</sub> than mitigation saves. Such trends might occur for of up to ten years. Certainty that mitigation causes trend reductions is only reached after ten or fifteen years, respectively of the type of causation.<br>Our analysis showcases the inherent uncertainty of near-term CO<sub>2</sub> projections. Assessments of the efficacy of mitigation in the near term are incomplete without quantitatively considering internal variability.</p>

The North Atlantic Ocean as a Modulator of Vegetation Greening/Browning in the Northern High Latitudes?

2021 ◽  
Author(s):  
Leonard F. Borchert ◽  
Alexander J. Winkler
Keyword(s):  
Climate Variability ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Internal Variability ◽  
Dominant Mode ◽  
High Latitudes ◽  
Model Simulations ◽  
Internal Climate Variability ◽  
The North ◽  
The North Atlantic

<p>Vegetation in the northern high latitudes shows a characteristic pattern of persistent changes as documented by multi-decadal satellite observations. The prevailing explanation that these mainly increasing trends (greening) are a consequence of external CO<sub>2</sub> forcing, i.e., due to the ubiquitous effect of CO2-induced fertilization and/or warming of temperature-limited ecosystems, however does not explain why some areas also show decreasing trends of vegetation cover (browning). We propose here to consider the dominant mode of multi-decadal internal climate variability in the north Atlantic region, the Atlantic Multidecadal Variability (AMV), as the missing link in the explanation of greening and browning trend patterns in the northern high latitudes. Such a link would also imply potential for decadal predictions of ecosystem changes in the northern high latitudes.</p><p>An analysis of observational and reanalysis data sets for the period 1979-2019 shows that locations characterized by greening trends largely coincide with warming summer temperature and increasing precipitation. Wherever either cooling or decreasing precipitation occurs, browning trends are observed over this period. These precipitation and temperature patterns are significantly correlated with a North Atlantic sea surface temperature index that represents the AMV signal, indicating its role in modulating greening/browning trend patterns in the northern high latitudes.</p><p>Using two large ensembles of coupled Earth system model simulations (100 members of MPI-ESM-LR Grand Ensemble and 32 members of the IPSL-CM6A-LR Large Ensemble), we separate the greening/browning pattern caused by external CO<sub>2</sub> forcing from that caused by internal climate variability associated with the AMV. These sets of model simulations enable a clean separation of the externally forced signal from internal variability. While the greening and browning patterns in the simulations do not agree with observations in terms of magnitude and location, we find consistent internally generated greening/browning patterns in both models caused by changes in temperature and precipitation linked to the AMV signal. These greening/browning trend patterns are of the same magnitude as those caused by the external forcing alone. Our work therefore shows that internally-generated changes of vegetation in the northern lands, driven by AMV, are potentially as large as those caused by external CO<sub>2</sub> forcing. We thus argue that the observed pattern of greening/browning in the northern high latitudes could originate from the combined effect of rising CO<sub>2</sub> as well as the AMV.</p>

Alternate history: A synthetic ensemble of ocean chlorophyll concentrations

2021 ◽  
Author(s):  
Geneviève Elsworth ◽  
Nicole Lovenduski ◽  
Karen McKinnon
Keyword(s):  
Climate Variability ◽  
Internal Variability ◽  
Ensemble Member ◽  
Global Ocean ◽  
Large Ensemble ◽  
Internal Climate Variability ◽  
Surface Ocean ◽  
Large Ensembles ◽  
Long Term Trends

<p>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 phasing of internal climate variability. We further demonstrate the validity of our statistical approach by recreating a 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. Our results suggest the potential to explore this approach for other ocean biogeochemical variables.</p>

Comparing inter-annual variabilities in three regional single model initial-condition large ensembles (SMILE) over Europe

2020 ◽  
Author(s):  
Fabian von Trentini ◽  
Emma E. Aalbers ◽  
Erich M. Fischer ◽  
Ralf Ludwig
Keyword(s):  
Climate Variability ◽  
Heat Waves ◽  
Internal Variability ◽  
Summer Temperature ◽  
Winter Temperature ◽  
Single Model ◽  
Internal Climate Variability ◽  
Annual Variability ◽  
Temperature And Precipitation ◽  
Large Ensembles

<p>Single model large ensembles are widely used model experiments to estimate internal climate variability (here: inter-annual variability). The underlying assumption is that the internal variability of the chosen model is a good approximation of the observed natural variability. In this study, for the first time over Europe, we test this assumption based on the comparison of three regional climate model large ensembles (16 members of an EC-EARTH-RACMO ensemble, 21 members of a CESM-CCLM ensemble, 50 members of a CanESM-CRCM ensemble) for four European domains (British Isles, France, Mid-Europe, Alps). Simulated inter-annual variability is evaluated against E-OBS and the inter-annual variability and its future change are compared across the ensembles. Analyses comprise seasonal temperature and precipitation, as well as indicators for dry periods and heat waves. Results show a large consistency of all three ensembles with E-OBS data for most indicators and regions, validating the abilities of these ensembles to represent natural variability on the annual scale. EC-EARTH-RACMO shows the highest inter-annual variability for winter temperature and precipitation, whereas CESM-CCLM shows the highest variability for summer temperature and precipitation, as well as for heatwaves and dry periods. Despite these model differences, the sign of the future changes in internal variability is largely the same in all models: for summer temperature, summer precipitation and the number of heat waves, the internal variability increases, while it decreases for winter temperature. While dry periods reveal a tendency to increase in variability, the changes of winter precipitation remain less conclusive. The overall consistency across single model large ensembles and observations strengthens the concept of large ensembles, and underlines their great potential for understanding and quantifying internal climate variability and its role in climate change dynamics.</p>

scholarly journals Wind speed stilling and its recovery due to internal climate variability

2021 ◽  
Vol 12 (4) ◽  
pp. 1239-1251
Author(s):  
Jan Wohland ◽  
Doris Folini ◽  
Bryn Pickering
Keyword(s):  
Wind Speed ◽  
Climate Variability ◽  
Internal Variability ◽  
Systematic Investigation ◽  
Max Planck ◽  
Near Surface ◽  
Climate Conditions ◽  
Wind Speeds ◽  
Internal Climate Variability ◽  
Long Run

Abstract. Near-surface winds affect many processes on planet Earth, ranging from fundamental biological mechanisms such as pollination to man-made infrastructure that is designed to resist or harness wind. The observed systematic wind speed decline up to around 2010 (stilling) and its subsequent recovery have therefore attracted much attention. While this sequence of downward and upwards trends and good connections to well-established modes of climate variability suggest that stilling could be a manifestation of multidecadal climate variability, a systematic investigation is currently lacking. Here, we use the Max Planck Institute Grand Ensemble (MPI-GE) to decompose internal variability from forced changes in wind speeds. We report that wind speed changes resembling observed stilling and its recovery are well in line with internal climate variability, both under current and future climate conditions. Moreover, internal climate variability outweighs forced changes in wind speeds on 20-year timescales by 1 order of magnitude, despite the fact that smaller, forced changes become relevant in the long run as they represent alterations of mean states. In this regard, we reveal that land use change plays a pivotal role in explaining MPI-GE ensemble-mean wind changes in the representative concentration pathways 2.6, 4.5, and 8.5. Our results demonstrate that multidecadal wind speed variability is of greater relevance than forced changes over the 21st century, in particular for wind-related infrastructure like wind energy.

Separating Internal Variability from the Externally Forced Climate Response

2015 ◽  
Vol 28 (20) ◽  
pp. 8184-8202 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Low Frequency ◽  
Internal Variability ◽  
Atlantic Multidecadal Oscillation ◽  
Ensemble Member ◽  
Internal Climate Variability ◽  
Climate Simulations ◽  
Scaling Methods ◽  
Synthetic Time Series

Abstract Separating low-frequency internal variability of the climate system from the forced signal is essential to better understand anthropogenic climate change as well as internal climate variability. Here both synthetic time series and the historical simulations from phase 5 of CMIP (CMIP5) are used to examine several methods of performing this separation. Linear detrending, as is commonly used in studies of low-frequency climate variability, is found to introduce large biases in both amplitude and phase of the estimated internal variability. Using estimates of the forced signal obtained from ensembles of climate simulations can reduce these biases, particularly when the forced signal is scaled to match the historical time series of each ensemble member. These so-called scaling methods also provide estimates of model sensitivities to different types of external forcing. Applying the methods to observations of the Atlantic multidecadal oscillation leads to different estimates of the phase of this mode of variability in recent decades.

scholarly journals Robust detection of forced warming in the presence of potentially large climate variability

2021 ◽  
Author(s):  
Sebastian Sippel ◽  
Nicolai Meinshausen ◽  
Eniko Székely ◽  
Erich Fischer ◽  
Angeline G. Pendergrass ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Models ◽  
Climate Model ◽  
Decadal Variability ◽  
Internal Variability ◽  
Warming Trend ◽  
Robust Detection ◽  
Internal Climate Variability ◽  
Detection And Attribution

<p>Warming of the climate system is unequivocal and substantially exceeds unforced internal climate variability. Detection and attribution (D&A) employs spatio-temporal fingerprints of the externally forced climate response to assess the magnitude of a climate signal, such as the multi-decadal global temperature trend, while internal variability is often estimated from unforced (“control”) segments of climate model simulations (e.g. Santer et al. 2019). Estimates of the exact magnitude of decadal-scale internal variability, however, remain uncertain and are limited by relatively short observed records, their entanglement with the forced response, and considerable spread of simulated variability across climate models. Hence, a limitation of D&A is that robustness and confidence levels depend on the ability of climate models to correctly simulate internal variability (Bindoff et al., 2013).</p><p>For example, the large spread in simulated internal variability across climate models implies that the observed 40-year global mean temperature trend of about 0.76°C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5σ), corresponding to vanishingly small probabilities if taken at face value. But the observed trend would exceed the standard deviation of a few `high-variability' climate models `only' by a factor of about two, thus unlikely to be internally generated but not practically impossible given unavoidable climate system and observational uncertainties. This illustrates the key role of model uncertainty in the simulation of internal variability for D&A confidence estimates.</p><p>Here we use a novel statistical learning method to extract a fingerprint of climate change that is robust towards model differences and internal variability, even of large amplitude. We demonstrate that externally forced warming is distinct from internal variability and detectable with high confidence on any state-of-the-art climate model, even those that simulate the largest magnitude of unforced multi-decadal variability. Based on the median of all models, it is extremely likely that more than 85% of the observed warming trend over the last 40 years is externally driven. Detection remains robust even if their main modes of decadal variability would be scaled by a factor of two. It is extremely likely that at least 55% of the observed warming trend over the last 40 years cannot be explained by internal variability irrespective of which climate model’s natural variability estimates are used.</p><p>Our analysis helps to address this limitation in attributing warming to external forcing and provides a novel perspective for quantifying the magnitude of forced climate change even under uncertain but potentially large multi-decadal internal climate variability. This opens new opportunities to make D&A fingerprints robust in the presence of poorly quantified yet important features inextricably linked to model structural uncertainty, and the methodology may contribute to more robust detection and attribution of climate change to its various drivers.</p><p> </p><p>Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.</p><p>Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science. <em>Nat Clim Change</em> <strong>9</strong>(3), pp. 180-182.</p>

scholarly journals Constraints on long term warming in a climate mitigation scenario

2020 ◽  
Author(s):  
Benjamin Sanderson
Keyword(s):  
Carbon Budget ◽  
System Response ◽  
Climate Mitigation ◽  
Earth System ◽  
Mitigation Scenario ◽  
The Earth ◽  
Net Zero ◽  
Negative Emissions ◽  
Near Term

Abstract. Cumulative emissions budgets and net-zero emission target dates are often used to frame climate negotiations (Frameet al., 2014; Millar et al., 2016; Van Vuuren et al., 2016; Rogelj et al., 2015b; Matthews et al., 2012). However, their utilityfor near-term policy decisions is confounded by an uncertainties in future negative emissions capacity (Fuss et al., 2014; Smith et al., 2016; Larkin et al., 2018; Anderson and Peters, 2016) and in long term Earth System response to climate forcers(Rugenstein et al., 2019; Knutti et al., 2017; Armour, 2017) which may impact the utility of an indefinite carbon budget if peak temperatures occur significantly after net zero emissions are achieved, the likelihood of which in a simple model is conditionalon prior assumptions about the long term dynamics of the Earth System. Here we illustrate that the risks associated with nearterm positive emissions can be framed using a definite cumulative emissions budget with a 2040 time horizon, allowing thenecessity and scope for negative emissions deployment later in the century to be better informed by observed warming overcoming decades.

scholarly journals Evaluation of Leading Modes of Climate Variability in the CMIP Archives

2020 ◽  
Vol 33 (13) ◽  
pp. 5527-5545 ◽  
Author(s):  
John T. Fasullo ◽  
A. S. Phillips ◽  
C. Deser
Keyword(s):  
Climate Variability ◽  
Southern Oscillation ◽  
Internal Variability ◽  
Intrinsic Variability ◽  
Annular Modes ◽  
Internal Climate Variability ◽  
The North ◽  
The Pacific ◽  
The North Atlantic Oscillation ◽  
Modes Of Climate Variability

AbstractThe adequate simulation of internal climate variability is key for our understanding of climate as it underpins efforts to attribute historical events, predict on seasonal and decadal time scales, and isolate the effects of climate change. Here the skill of models in reproducing observed modes of climate variability is assessed, both across and within the CMIP3, CMIP5, and CMIP6 archives, in order to document model capabilities, progress across ensembles, and persisting biases. A focus is given to the well-observed tropical and extratropical modes that exhibit small intrinsic variability relative to model structural uncertainty. These include El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the North Atlantic Oscillation (NAO), and the northern and southern annular modes (NAM and SAM). Significant improvements are identified in models’ representation of many modes. Canonical biases, which involve both amplitudes and patterns, are generally reduced across model generations. For example, biases in ENSO-related equatorial Pacific sea surface temperature, which extend too far westward, and associated atmospheric teleconnections, which are too weak, are reduced. Stronger tropical expression of the PDO in successive CMIP generations has characterized their improvement, with some CMIP6 models generating patterns that lie within the range of observed estimates. For the NAO, NAM, and SAM, pattern correlations with observations are generally higher than for other modes and slight improvements are identified across successive model generations. For ENSO and PDO spectra and extratropical modes, changes are small compared to internal variability, precluding definitive statements regarding improvement.

scholarly journals Consistently Estimating Internal Climate Variability from Climate Model Simulations

2017 ◽  
Vol 30 (23) ◽  
pp. 9555-9573 ◽  
Author(s):  
Dirk Olonscheck ◽  
Dirk Notz
Keyword(s):  
Climate Variability ◽  
Sea Ice ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
New Method ◽  
Polar Regions ◽  
Internal Climate Variability ◽  
Ensemble Simulations

This paper introduces and applies a new method to consistently estimate internal climate variability for all models within a multimodel ensemble. The method regresses each model’s estimate of internal variability from the preindustrial control simulation on the variability derived from a model’s ensemble simulations, thus providing practical evidence of the quasi-ergodic assumption. The method allows one to test in a multimodel consensus view how the internal variability of a variable changes for different forcing scenarios. Applying the method to the CMIP5 model ensemble shows that the internal variability of global-mean surface air temperature remains largely unchanged for historical simulations and might decrease for future simulations with a large CO2 forcing. Regionally, the projected changes reveal likely increases in temperature variability in the tropics, subtropics, and polar regions, and extremely likely decreases in midlatitudes. Applying the method to sea ice volume and area shows that their respective internal variability likely or extremely likely decreases proportionally to their mean state, except for Arctic sea ice area, which shows no consistent change across models. For the evaluation of CMIP5 simulations of Arctic and Antarctic sea ice, the method confirms that internal variability can explain most of the models’ deviation from observed trends but often not the models’ deviation from the observed mean states. The new method benefits from a large number of models and long preindustrial control simulations, but it requires only a small number of ensemble simulations. The method allows for consistent consideration of internal variability in multimodel studies and thus fosters understanding of the role of internal variability in a changing climate.

scholarly journals Timing of human-induced climate change emergence from internal climate variability for hydrological impact studies

2018 ◽  
Vol 49 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Mei-Jia Zhuan ◽  
Jie Chen ◽  
Ming-Xi Shen ◽  
Chong-Yu Xu ◽  
Hua Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Models ◽  
Climate Model ◽  
Climate Trends ◽  
Impact Studies ◽  
Internal Climate Variability ◽  
Temperature And Precipitation ◽  
Hydrological Impact ◽  
Near Term

Abstract This study proposes a method to estimate the timing of human-induced climate change (HICC) emergence from internal climate variability (ICV) for hydrological impact studies based on climate model ensembles. Specifically, ICV is defined as the inter-member difference in a multi-member ensemble of a climate model in which human-induced climate trends have been removed through a detrending method. HICC is defined as the mean of multiple climate models. The intersection between HICC and ICV curves is defined as the time of emergence (ToE) of HICC from ICV. A case study of the Hanjiang River watershed in China shows that the temperature change has already emerged from ICV during the last century. However, the precipitation change will be masked by ICV up to the middle of this century. With the joint contributions of temperature and precipitation, the ToE of streamflow occurs about one decade later than that of precipitation. This implies that consideration for water resource vulnerability to climate should be more concerned with adaptation to ICV in the near-term climate (present through mid-century), and with HICC in the long-term future, thus allowing for more robust adaptation strategies to water transfer projects in China.

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