Diverse responses of global‐mean surface temperature to external forcings and internal climate variability in observations and CMIP6 models

Abstract Changes in Earth’s climate are influenced by internal climate variability and external forcings, such as changes in solar radiation, volcanic eruptions, anthropogenic greenhouse gases (GHG), and aerosols. Although the response of surface temperature to external forcings has been studied extensively, this has not been done for sea level. Here, a range of climate model experiments for the twentieth century is used to study the response of global and regional sea level change to external climate forcings. Both the global mean thermosteric sea level and the regional dynamic sea level patterns show clear responses to anthropogenic forcings that are significantly different from internal climate variability and larger than the difference between models driven by the same external forcing. The regional sea level patterns are directly related to changes in surface winds in response to the external forcings. The spread between different realizations of the same model experiment is consistent with internal climate variability derived from preindustrial control simulations. The spread between the different models is larger than the internal variability, mainly in regions with large sea level responses. Although the sea level responses to GHG and anthropogenic aerosol forcing oppose each other in the global mean, there are differences on a regional scale, offering opportunities for distinguishing between these two forcings in observed sea level change.

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Identifying Signatures of Natural Climate Variability in Time Series of Global-Mean Surface Temperature: Methodology and Insights

Journal of Climate ◽

10.1175/2009jcli3089.1 ◽

2009 ◽

Vol 22 (22) ◽

pp. 6120-6141 ◽

Cited By ~ 111

Author(s):

David W. J. Thompson ◽

John M. Wallace ◽

Phil D. Jones ◽

John J. Kennedy

Keyword(s):

Time Series ◽

Climate Variability ◽

Surface Temperature ◽

High Latitude ◽

Volcanic Eruptions ◽

Natural Variability ◽

Natural Climate Variability ◽

Global Mean Surface Temperature ◽

Natural Climate ◽

Global Mean

Abstract Global-mean surface temperature is affected by both natural variability and anthropogenic forcing. This study is concerned with identifying and removing from global-mean temperatures the signatures of natural climate variability over the period January 1900–March 2009. A series of simple, physically based methodologies are developed and applied to isolate the climate impacts of three known sources of natural variability: the El Niño–Southern Oscillation (ENSO), variations in the advection of marine air masses over the high-latitude continents during winter, and aerosols injected into the stratosphere by explosive volcanic eruptions. After the effects of ENSO and high-latitude temperature advection are removed from the global-mean temperature record, the signatures of volcanic eruptions and changes in instrumentation become more clearly apparent. After the volcanic eruptions are subsequently filtered from the record, the residual time series reveals a nearly monotonic global warming pattern since ∼1950. The results also reveal coupling between the land and ocean areas on the interannual time scale that transcends the effects of ENSO and volcanic eruptions. Globally averaged land and ocean temperatures are most strongly correlated when ocean leads land by ∼2–3 months. These coupled fluctuations exhibit a complicated spatial signature with largest-amplitude sea surface temperature perturbations over the Atlantic Ocean.

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Effects of strongly eddying oceans on multidecadal climate variability in the Community Earth System Model

Ocean Science ◽

10.5194/os-17-1251-2021 ◽

2021 ◽

Vol 17 (5) ◽

pp. 1251-1271

Author(s):

André Jüling ◽

Anna von der Heydt ◽

Henk A. Dijkstra

Keyword(s):

Climate Variability ◽

Surface Temperature ◽

Sea Surface ◽

System Model ◽

Earth System ◽

Multidecadal Variability ◽

Global Mean Surface Temperature ◽

Ocean Flows ◽

Community Earth System Model ◽

Global Mean

Abstract. Climate variability on multidecadal timescales appears to be organized in pronounced patterns with clear expressions in sea surface temperature, such as the Atlantic Multidecadal Variability and the Pacific Decadal Oscillation. These patterns are now well studied both in observations and global climate models and are important in the attribution of climate change. Results from CMIP5 models have indicated large biases in these patterns with consequences for ocean heat storage variability and the global mean surface temperature. In this paper, we use two multi-century Community Earth System Model simulations at coarse (1∘) and fine (0.1∘) ocean model horizontal grid spacing to study the effects of the representation of mesoscale ocean flows on major patterns of multidecadal variability. We find that resolving mesoscale ocean flows both improves the characteristics of the modes of variability with respect to observations and increases the amplitude of the heat content variability in the individual ocean basins. In the strongly eddying model, multidecadal variability increases compared to sub-decadal variability. This shift of spectral power is seen in sea surface temperature indices, basin-scale surface heat fluxes, and the global mean surface temperature. This implies that the current CMIP6 model generation, which predominantly does not resolve the ocean mesoscale, may systematically underestimate multidecadal variability.

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Effects of strongly eddying oceans on multidecadal climate variability in the Community Earth System Model

10.5194/os-2020-85 ◽

2020 ◽

Author(s):

André Jüling ◽

Anna von der Heydt ◽

Henk A. Dijkstra

Keyword(s):

Climate Variability ◽

Surface Temperature ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Multidecadal Variability ◽

Global Mean Surface Temperature ◽

Ocean Flows ◽

Community Earth System Model ◽

Global Mean

Abstract. Climate variability on multidecadal time scales appears to be organized in pronounced patterns with clear expressions in sea surface temperature, such as the Atlantic Multidecadal Variability and the Pacific Decadal Oscillation. These patterns are now well studied both in observations and global climate models and are important in the attribution of climate change. Results from CMIP5 models have indicated large biases in these patterns with consequences for ocean heat storage variability and eventually the global mean surface temperature. In this paper, we use two multi-century Community Earth System Model simulations at coarse (1°) and fine (0.1°) ocean model horizontal grid spacing to study the effects of the representation of mesoscale ocean flows on major patterns of multidecadal variability. We find that resolving mesoscale ocean flows both improves the characteristics of the modes of variability with respect to observations and increases the amplitude of the heat content variability in the individual ocean basins. The effect on the global mean surface temperature is relatively minor.

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Effects of mesoscale ocean flows on multidecadal climate variability

10.5194/egusphere-egu2020-20598 ◽

2020 ◽

Cited By ~ 1

Author(s):

André Jüling ◽

Anna von der Heydt ◽

Henk Dijkstra

Keyword(s):

Climate Variability ◽

Surface Temperature ◽

Climate Models ◽

Heat Content ◽

Global Climate Models ◽

Cmip5 Models ◽

Multidecadal Variability ◽

Global Mean Surface Temperature ◽

Ocean Flows ◽

Global Mean

<div> <div>Climate variability on decadal to multidecadal time scales appears to be organized in pronounced patterns with clear expressions in sea surface temperature, such as the Pacific Multidecadal Variability and the Atlantic Multidecadal Variability. These patterns are now well studied both in observations and in global climate models and are important in the attribution of climate change. Results in CMIP5 models have indicated large biases in these patterns with consequences for ocean heat storage variability and eventually the global mean surface temperature.</div> <div>We use two multi-century Community Earth System Model simulations at coarse (1&#176;) and fine (0.1&#176;) ocean model horizontal grid spacing and study the effect of the representation of mesoscale ocean flows on major patterns of multidecadal variability. We find that resolving mesoscale ocean flows both improves the characteristics of the modes of variability with respect to observations and increases the amplitude of the heat content variability in the individual ocean basins. However, the effect on the global mean surface temperature is relatively minor.</div> </div>

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Model for estimating the transient response of the global mean surface temperature to changes in the concentrations of atmospheric aerosols and radiatively-active gases

Оптика атмосферы и океана ◽

10.15372/aoo20190409 ◽

2019 ◽

Keyword(s):

Surface Temperature ◽

Transient Response ◽

Atmospheric Aerosols ◽

Global Mean Surface Temperature ◽

Global Mean

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Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

10.21203/rs.3.rs-1103739/v1 ◽

2021 ◽

Author(s):

Philip G. Sansom ◽

Donald Cummins ◽

Stefan Siegert ◽

David B Stephenson

Keyword(s):

Time Series ◽

Global Warming ◽

Surface Temperature ◽

Climate Model ◽

Future Projections ◽

Perfect Model ◽

Global Mean Surface Temperature ◽

Time Series Approach ◽

Energy Balance Models ◽

Global Mean

Abstract Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

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Climate variability following large volcanic eruption: CMIP6 model investigation

10.5194/egusphere-egu21-3686 ◽

2021 ◽

Author(s):

Elizaveta Malinina ◽

Nathan Gillett

Keyword(s):

Climate Variability ◽

Volcanic Eruption ◽

Volcanic Eruptions ◽

Arctic Sea Ice ◽

Evaluation Tool ◽

Global Mean Temperature ◽

Arctic Sea ◽

Global Mean Surface Temperature ◽

Krakatoa Eruption ◽

Global Mean

<p>Volcanic eruptions are an important driver of climate variability. Multiple literature sources have shown that after large explosive eruptions there is a decrease in global mean temperature, caused by an increased amount of stratospheric aerosols which influence the global radiative budget. In this study, we investigate the changes in several climate variables after a volcanic eruption. Using ESMValTool (Earth System Model Evaluation Tool) on an ensemble of historical simulations from CMIP6, such variables as global mean surface temperature (GMST), Arctic sea ice area and Nino 3.4 index were analyzed following the 1883 Krakatoa eruption. While there is a definite decrease in the multi-model mean GMST after the eruption, other indices do not show as prominent change. The reasons for this behavior are under investigation.&#160;</p>

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