Consistently Estimating Internal Climate Variability from Climate Model 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.

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Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

10.5194/egusphere-egu21-3106 ◽

2021 ◽

Author(s):

Vladimir Semenov ◽

Tatiana Matveeva

Keyword(s):

Sea Ice ◽

20Th Century ◽

Climate Model ◽

Surface Air Temperature ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

The North ◽

Arctic Sea ◽

Ice Concentration

Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20th century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model&#8217;s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20th century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km2 in September and 0.7 million km2 in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21st century.

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Arctic Sea Ice Reemergence: The Role of Large-Scale Oceanic and Atmospheric Variability*

Journal of Climate ◽

10.1175/jcli-d-14-00354.1 ◽

2015 ◽

Vol 28 (14) ◽

pp. 5477-5509 ◽

Cited By ~ 25

Author(s):

Mitchell Bushuk ◽

Dimitrios Giannakis ◽

Andrew J. Majda

Keyword(s):

Sea Ice ◽

Large Scale ◽

Climate Model ◽

Surface Air Temperature ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Arctic Sea ◽

Spring Sea Ice ◽

Phase Relationships

Abstract Arctic sea ice reemergence is a phenomenon in which spring sea ice anomalies are positively correlated with fall anomalies, despite a loss of correlation over the intervening summer months. This work employs a novel data analysis algorithm for high-dimensional multivariate datasets, coupled nonlinear Laplacian spectral analysis (NLSA), to investigate the regional and temporal aspects of this reemergence phenomenon. Coupled NLSA modes of variability of sea ice concentration (SIC), sea surface temperature (SST), and sea level pressure (SLP) are studied in the Arctic sector of a comprehensive climate model and in observations. It is found that low-dimensional families of NLSA modes are able to efficiently reproduce the prominent lagged correlation features of the raw sea ice data. In both the model and observations, these families provide an SST–sea ice reemergence mechanism, in which melt season (spring) sea ice anomalies are imprinted as SST anomalies and stored over the summer months, allowing for sea ice anomalies of the same sign to reappear in the growth season (fall). The ice anomalies of each family exhibit clear phase relationships between the Barents–Kara Seas, the Labrador Sea, and the Bering Sea, three regions that compose the majority of Arctic sea ice variability. These regional phase relationships in sea ice have a natural explanation via the SLP patterns of each family, which closely resemble the Arctic Oscillation and the Arctic dipole anomaly. These SLP patterns, along with their associated geostrophic winds and surface air temperature advection, provide a large-scale teleconnection between different regions of sea ice variability. Moreover, the SLP patterns suggest another plausible ice reemergence mechanism, via their winter-to-winter regime persistence.

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Role of the internal climate variability in the atmospheric response to a sudden summer Arctic sea ice loss

10.5194/egusphere-egu21-2528 ◽

2021 ◽

Author(s):

Steve Delhaye ◽

Thierry Fichefet ◽

François Massonnet ◽

David Docquier ◽

Christopher Roberts ◽

...

Keyword(s):

Climate Variability ◽

Sea Ice ◽

Signal To Noise Ratio ◽

Horizontal Resolution ◽

Arctic Sea Ice ◽

Atmospheric Response ◽

Signal To Noise ◽

Internal Climate Variability ◽

Arctic Sea ◽

Arctic Sea Ice Loss

The retreat of Arctic sea ice for the last four decades is a primary manifestation of the climate system response to increasing atmospheric greenhouse gas concentrations. This retreat is frequently considered as a possible driver of atmospheric circulation anomalies at mid-latitudes. However, the year-to-year evolution of the Arctic sea ice cover is also characterized by significant fluctuations attributed to internal climate variability. It is unclear how the atmosphere will respond to a near-total retreat of summer Arctic sea ice, a reality that might occur in the foreseeable future. This study uses sensitivity experiments&#160; with higher and lower horizontal resolution configurations of three global coupled climate models to investigate the local and remote atmospheric responses to a reduction in Arctic sea ice cover during the preceding summer. Recognizing that these responses likely depend on the model itself and on its horizontal resolution, and that the model&#8217;s internally-generated climate variability may obscure the atmospheric response, we design a protocol to compare each source separately. After imposing a 15-month albedo perturbation resulting in a sudden summer Arctic sea ice loss, the remote mid-latitude climate response has a very low signal-to-noise ratio such that internal climate variability dominates the uncertainty of the response, regardless of the atmospheric variable. Indeed, more than 28, 165 and 210 members are needed to detect a robust response in surface air temperature, precipitation and sea level pressure to sea ice loss in Europe, respectively. Finally, we find that horizontal resolution plays a secondary role in the uncertainty of the atmospheric response to substantial perturbation of Arctic sea ice. These findings suggest that even with higher resolution model configurations, it is important to have large ensemble sizes to increase the signal to noise ratio for the mid-latitude atmospheric response to sea ice changes.

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On the Origin of the Surface Air Temperature Difference between the Hemispheres in Earth's Present-Day Climate

Journal of Climate ◽

10.1175/jcli-d-12-00636.1 ◽

2013 ◽

Vol 26 (18) ◽

pp. 7136-7150 ◽

Cited By ~ 41

Author(s):

Georg Feulner ◽

Stefan Rahmstorf ◽

Anders Levermann ◽

Silvia Volkwardt

Keyword(s):

Sea Ice ◽

Heat Transport ◽

Air Temperature ◽

Ocean Circulation ◽

Temperature Difference ◽

Greenhouse Gas Emission ◽

Surface Air Temperature ◽

Arctic Sea Ice ◽

Polar Regions ◽

Additional Contribution

Abstract In today's climate, the annually averaged surface air temperature in the Northern Hemisphere (NH) is 1°–2°C higher than in the Southern Hemisphere (SH). Historically, this interhemispheric temperature difference has been attributed to a number of factors, including seasonal differences in insolation, the larger area of (tropical) land in the NH, the particularities of the Antarctic in terms of albedo and temperature, and northward heat transport by ocean circulation. A detailed investigation of these factors and their contribution to the temperature difference, however, has to the authors' knowledge not been performed so far. Here the origin of the interhemispheric temperature difference is traced using an assessment of climatological data and the observed energy budget of Earth as well as model simulations. It is found that for the preindustrial climate the temperature difference is predominantly due to meridional heat transport in the oceans, with an additional contribution from the albedo differences between the polar regions. The combination of these factors (that are to some extent coupled) governs the evolution of the temperature difference over the past millennium. Since the beginning of industrialization the interhemispheric temperature difference has increased due to melting of sea ice and snow in the NH. Furthermore, the predicted higher rate of warming over land as compared to the oceans contributes to this increase. Simulations for the twenty-first century show that the interhemispheric temperature difference continues to grow for the highest greenhouse gas emission scenarios due to the land–ocean warming contrast and the strong loss of Arctic sea ice, whereas the decrease in overturning strength dominates for the more moderate scenarios.

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

10.5194/egusphere-egu21-9611 ◽

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

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 (&#8220;control&#8221;) 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).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&#176;C (1980-2019) would exceed the standard deviation of internally generated variability of a set of `low variability' models by far (> 5&#963;), 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.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&#8217;s natural variability estimates are used.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.&#160;Bindoff, N.L., et al., 2013. Detection and attribution of climate change: from global to regional. IPCC AR5, WG1, Chapter 10.Santer, B.D., et al., 2019. Celebrating the anniversary of three key events in climate change science.&#160;Nat Clim Change&#160;9(3), pp. 180-182.

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The roles of winds and waves in Arctic sea ice variability

10.5194/egusphere-egu21-10260 ◽

2021 ◽

Author(s):

Lettie Roach ◽

Edward Blanchard-Wrigglesworth ◽

Cecilia Bitz

Keyword(s):

Sea Ice ◽

Prediction Models ◽

Climate Model ◽

Internal Variability ◽

Arctic Sea Ice ◽

Near Surface ◽

Coupled Modelling ◽

Ocean Surface Waves ◽

Air Temperatures ◽

Arctic Sea

It is broadly accepted that variability and trends in Arctic sea ice remain poorly simulated even in the most state-of-the-art coupled climate and climate prediction models. Here, we show that a modern coupled climate model (CESM1) is in fact able to reproduce the observed variability and decline in summer sea ice when winds are nudged towards values from reanalysis.&#160; We argue that the nudged-winds framework provides a straightforward way of evaluating models by removing much of the contribution of internal variability, revealing model successes and biases. The results demonstrate the importance of atmospheric circulation in driving interannual variability in sea ice and near-surface air temperatures, particularly in the summer. Finally, we will discuss the potential role of ocean surface waves in driving variability in Arctic sea ice, based on observational analysis and new coupled modelling results.

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Seasonal transition dates can reveal biases in Arctic sea ice simulations

10.5194/tc-2020-81 ◽

2020 ◽

Author(s):

Abigail Smith ◽

Alexandra Jahn ◽

Muyin Wang

Keyword(s):

Sea Ice ◽

Annual Cycle ◽

Model Evaluation ◽

Climate Model ◽

Internal Variability ◽

Arctic Sea Ice ◽

Seasonal Transition ◽

Climate Model Evaluation ◽

Arctic Sea ◽

Break Up

Abstract. Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, break-up, opening, freeze onset, freeze-up and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1.5–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions of melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons.

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Marginal ice zone fraction benchmarks sea ice and climate model skill

Nature Communications ◽

10.1038/s41467-021-22004-7 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Christopher Horvat

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Internal Variability ◽

Arctic Sea Ice ◽

Global Climate Models ◽

Global Mean Temperature ◽

Marginal Ice Zone ◽

The Impact

AbstractGlobal climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming. Modeled SIA losses are highly correlated to global mean temperature increases, making it challenging to gauge if improvements in modeled sea ice derive from improved sea-ice models or from improvements in forcing driven by other GCM components. I use a set of five large GCM ensembles, and CMIP6 simulations, to quantify GCM internal variability and variability between GCMs from 1979–2014, showing modern GCMs do not plausibly estimate the response of SIA to warming in all months. I identify the marginal ice zone fraction (MIZF) as a metric that is less correlated to warming, has a response plausibly simulated from January–September (but not October–December), and has highly variable future projections across GCMs. These qualities make MIZF useful for evaluating the impact of sea-ice model changes on past, present, and projected sea-ice state.

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El Niño variability mediates 21st century growth effects of climate change

10.5194/egusphere-egu21-8023 ◽

2021 ◽

Author(s):

Christopher Callahan ◽

Justin Mankin

Keyword(s):

Climate Change ◽

Economic Growth ◽

Climate Variability ◽

Climate Model ◽

Internal Variability ◽

Climate Impact ◽

Growth Effects ◽

Internal Climate Variability ◽

Mitigation And Adaptation ◽

Model Ensembles

Understanding the effect of climate change on global economic growth is critical to informing optimal mitigation and adaptation policy. Many recent efforts have been made to empirically quantify the roles of weather and climate in economic growth, but these efforts have generally focused on changes in mean climate rather than changes in climate variability. Climate change is expected to alter modes of climate variability, so fully quantifying the costs of climate change requires both understanding the effects of climate variability on economic growth and constraining how this variability will evolve under forcing. Here we combine historical climate and economic data with multiple climate model ensembles to quantify the economic growth effects of El Ni&#241;o and examine how these effects evolve in the 21st century. Preliminary results show substantial negative effects of El Ni&#241;o on growth, with historical events reducing growth by >5 percentage points over 5 years in countries whose temperature variability is tightly correlated with ENSO. We then examine how climate change influences El Ni&#241;o and its growth effects in both multi-model and single-model ensembles, allowing us to isolate the role of internal climate variability in shaping the evolution of ENSO statistics in the 21st century. Climate change is generally projected to increase El Ni&#241;o frequency and thus the resulting growth penalties, but internal variability generates a wide spread of responses, all of which are consistent with the same forcing. These results highlight how internal variability can influence both interannual El Ni&#241;o occurrence and long-term changes in its statistics, with consequences for future economic growth. Moreover, these results illustrate the range of climate impact trajectories that are consistent with the same emissions, providing critical information for adaptation decision-makers needing to construct robust socioeconomic systems in the face of 21st century climate change.

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Seasonal transition dates can reveal biases in Arctic sea ice simulations

The Cryosphere ◽

10.5194/tc-14-2977-2020 ◽

2020 ◽

Vol 14 (9) ◽

pp. 2977-2997

Author(s):

Abigail Smith ◽

Alexandra Jahn ◽

Muyin Wang

Keyword(s):

Sea Ice ◽

Annual Cycle ◽

Model Evaluation ◽

Climate Model ◽

Internal Variability ◽

Arctic Sea Ice ◽

Seasonal Transition ◽

Ice Growth ◽

Climate Model Evaluation ◽

Arctic Sea

Abstract. Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, breakup, opening, freeze onset, freeze-up, and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions for melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability range, indicating the contribution of model differences. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons.

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