Simulation and Projection of Global Mean Surface Temperature Using the Empirical Model of Global Climate and Comparison to CMIP6 GCMs

Author(s):  
Laura McBride ◽  
Austin Hope ◽  
Timothy Canty ◽  
Walter Tribett ◽  
Brian Bennett ◽  
...  

<p>The Empirical Model of Global Climate (EM-GC) (Canty et al., ACP, 2013, McBride et al., ESDD, 2020) is a multiple linear regression, energy balance model that accounts for the natural influences on global mean surface temperature due to ENSO, the 11-year solar cycle, major volcanic eruptions, as well as the anthropogenic influence of greenhouse gases and aerosols and the efficiency of ocean heat uptake. First, we will analyze the human contribution of global warming from 1975-2014 based on the climate record, also known as the attributable anthropogenic warming rate (AAWR). We will compare the values of AAWR found using the EM-GC with values of AAWR from the CMIP6 multi-model ensemble. Preliminary analysis indicates that over the past three decades, the human component of global warming inferred from the CMIP6 GCMs is larger than the human component of warming from the climate record. Second, we will compare values of equilibrium climate sensitivity inferred from the historical climate record to those determined from CMIP6 GCMs using the Gregory et al., GRL, 2004 method. Third, we will use the future abundances of greenhouse gases and aerosols provided by the Shared Socioeconomic Pathways (SSPs) to project future global mean surface temperature change. We will compare the projections of future temperature anomalies from CMIP6 GCMs to those determined by the EM-GC. We will conclude by assessing the probability of the CMIP6 and EM-GC projections of achieving the Paris Agreement target (1.5°C) and upper limit (2.0°C) for several of the SSP scenarios.</p>

2021 ◽  
Author(s):  
Philip G. Sansom ◽  
Donald Cummins ◽  
Stefan Siegert ◽  
David B Stephenson

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.


2009 ◽  
Vol 22 (22) ◽  
pp. 6120-6141 ◽  
Author(s):  
David W. J. Thompson ◽  
John M. Wallace ◽  
Phil D. Jones ◽  
John J. Kennedy

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.


2017 ◽  
Vol 30 (2) ◽  
pp. 595-608 ◽  
Author(s):  
Ping Huang

Anomalous rainfall in the tropical Pacific driven by El Niño–Southern Oscillation (ENSO) is a crucial pathway of ENSO’s global impacts. The changes in ENSO rainfall under global warming vary among the models, even though previous studies have shown that many models project that ENSO rainfall will likely intensify and shift eastward in response to global warming. The present study evaluates the robustness of the changes in ENSO rainfall in 32 CMIP5 models forced under the representative concentration pathway 8.5 (RCP8.5) scenario. The robust increase in mean-state moisture dominates the robust intensification of ENSO rainfall. The uncertain amplitude changes in ENSO-related SST variability are the largest source of the uncertainty in ENSO rainfall changes through influencing the amplitude changes in ENSO-driven circulation variability, whereas the structural changes in ENSO SST and ENSO circulation enhancement in the central Pacific are more robust than the amplitude changes. The spatial pattern of the mean-state SST changes—the departure of local SST changes from the tropical mean—with an El Niño–like pattern is a relatively robust factor, although it also contains pronounced intermodel differences. The intermodel spread of historical ENSO circulation is another noteworthy source of the uncertainty in ENSO rainfall changes. The intermodel standard deviation of ENSO rainfall changes increases along with the increase in global-mean surface temperature. However, the robustness of enhanced ENSO rainfall changes in the central-eastern Pacific is almost unchanged, whereas the eastward shift of ENSO rainfall is increasingly robust along with the increase in global-mean surface temperature.


2015 ◽  
Vol 72 (8) ◽  
pp. 3281-3289 ◽  
Author(s):  
Meng Wei ◽  
Fangli Qiao ◽  
Jia Deng

Abstract Recent global warming hiatus has received much attention; however, a robust and quantitative definition for the hiatus is still lacking. Recent studies by Scafetta, Wu et al., and Tung and Zhou showed that multidecadal variability (MDV) is responsible for the multidecadal accelerated warming and hiatuses in historical global-mean surface temperature (GMST) records, though MDV itself has not received sufficient attention thus far. Here, the authors introduce four key episodes in GMST evolution, according to different phases of the MDV extracted by the ensemble empirical-mode decomposition method from the ensemble HadCRUT4 monthly GMST time series. The “warming (cooling) hiatus” and “typical warming (cooling)” periods are defined as the 95% confidence intervals for the locations of local MDV maxima (minima) and of their derivatives, respectively. Since 1850, the warming hiatuses, cooling hiatuses, and typical warming have already occurred three times and the typical cooling has occurred twice. At present, the MDV is in its third warming-hiatus period, which started in 2012 and would last until 2017, followed by a 30-yr cooling episode, while the trend will sustain the current steady growth in the next 50 years. Their superposition presents steplike rising since 1850. It is currently ascending a new height and will stay there until the next warming phase of the MDV carries it higher.


2017 ◽  
Vol 12 (5) ◽  
pp. 054010 ◽  
Author(s):  
Paul-Arthur Monerie ◽  
Marie-Pierre Moine ◽  
Laurent Terray ◽  
Sophie Valcke

2015 ◽  
Vol 28 (17) ◽  
pp. 6608-6625 ◽  
Author(s):  
Leon D. Rotstayn ◽  
Mark A. Collier ◽  
Drew T. Shindell ◽  
Olivier Boucher

Abstract Linear regression is used to examine the relationship between simulated changes in historical global-mean surface temperature (GMST) and global-mean aerosol effective radiative forcing (ERF) in 14 climate models from CMIP5. The models have global-mean aerosol ERF that ranges from −0.35 to −1.60 W m−2 for 2000 relative to 1850. It is shown that aerosol ERF is the dominant factor that determines intermodel variations in simulated GMST change: correlations between aerosol ERF and simulated changes in GMST exceed 0.9 for linear trends in GMST over all periods that begin between 1860 and 1950 and end between 1995 and 2005. Comparison of modeled and observed GMST trends for these time periods gives an inferred global-mean aerosol ERF of −0.92 W m−2. On average, transient climate sensitivity is roughly 40% larger with respect to historical forcing from aerosols than well-mixed greenhouse gases. This enhanced sensitivity explains the dominant effect of aerosol forcing on simulated changes in GMST: it is estimated that 85% of the intermodel variance of simulated GMST change is explained by variations in aerosol ERF, but without the enhanced sensitivity less than half would be explained. Physically, the enhanced sensitivity is caused by a combination of 1) the larger concentration of aerosol forcing in the midlatitudes of the Northern Hemisphere, where positive feedbacks are stronger and transient warming is faster than in the Southern Hemisphere, and 2) the time evolution of aerosol forcing, which levels out earlier than forcing from well-mixed greenhouse gases.


2021 ◽  
Vol 102 (1) ◽  
pp. E20-E37
Author(s):  
Blair Trewin ◽  
Anny Cazenave ◽  
Stephen Howell ◽  
Matthias Huss ◽  
Kirsten Isensee ◽  
...  

AbstractThe World Meteorological Organization has developed a set of headline indicators for global climate monitoring. These seven indicators are a subset of the existing set of essential climate variables (ECVs) established by the Global Climate Observing System and are intended to provide the most essential parameters representing the state of the climate system. These indicators include global mean surface temperature, global ocean heat content, state of ocean acidification, glacier mass balance, Arctic and Antarctic sea ice extent, global CO2 mole fraction, and global mean sea level. This paper describes how well each of these indicators are currently monitored, including the number and quality of the underlying datasets; the health of those datasets; observation systems used to estimate each indicator; the timeliness of information; and how well recent values can be linked to preindustrial conditions. These aspects vary widely between indicators. While global mean surface temperature is available in close to real time and changes from preindustrial levels can be determined with relatively low uncertainty, this is not the case for many other indicators. Some indicators (e.g., sea ice extent) are largely dependent on satellite data only available in the last 40 years, while some (e.g., ocean acidification) have limited underlying observational bases, and others (e.g., glacial mass balance) with data only available a year or more in arrears.


2019 ◽  
Vol 59 ◽  
pp. 14.1-14.101 ◽  
Author(s):  
V. Ramaswamy ◽  
W. Collins ◽  
J. Haywood ◽  
J. Lean ◽  
N. Mahowald ◽  
...  

Abstract We describe the historical evolution of the conceptualization, formulation, quantification, application, and utilization of “radiative forcing” (RF) of Earth’s climate. Basic theories of shortwave and longwave radiation were developed through the nineteenth and twentieth centuries and established the analytical framework for defining and quantifying the perturbations to Earth’s radiative energy balance by natural and anthropogenic influences. The insight that Earth’s climate could be radiatively forced by changes in carbon dioxide, first introduced in the nineteenth century, gained empirical support with sustained observations of the atmospheric concentrations of the gas beginning in 1957. Advances in laboratory and field measurements, theory, instrumentation, computational technology, data, and analysis of well-mixed greenhouse gases and the global climate system through the twentieth century enabled the development and formalism of RF; this allowed RF to be related to changes in global-mean surface temperature with the aid of increasingly sophisticated models. This in turn led to RF becoming firmly established as a principal concept in climate science by 1990. The linkage with surface temperature has proven to be the most important application of the RF concept, enabling a simple metric to evaluate the relative climate impacts of different agents. The late 1970s and 1980s saw accelerated developments in quantification, including the first assessment of the effect of the forcing due to the doubling of carbon dioxide on climate (the “Charney” report). The concept was subsequently extended to a wide variety of agents beyond well-mixed greenhouse gases (WMGHGs; carbon dioxide, methane, nitrous oxide, and halocarbons) to short-lived species such as ozone. The WMO and IPCC international assessments began the important sequence of periodic evaluations and quantifications of the forcings by natural (solar irradiance changes and stratospheric aerosols resulting from volcanic eruptions) and a growing set of anthropogenic agents (WMGHGs, ozone, aerosols, land surface changes, contrails). From the 1990s to the present, knowledge and scientific confidence in the radiative agents acting on the climate system have proliferated. The conceptual basis of RF has also evolved as both our understanding of the way radiative forcing drives climate change and the diversity of the forcing mechanisms have grown. This has led to the current situation where “effective radiative forcing” (ERF) is regarded as the preferred practical definition of radiative forcing in order to better capture the link between forcing and global-mean surface temperature change. The use of ERF, however, comes with its own attendant issues, including challenges in its diagnosis from climate models, its applications to small forcings, and blurring of the distinction between rapid climate adjustments (fast responses) and climate feedbacks; this will necessitate further elaboration of its utility in the future. Global climate model simulations of radiative perturbations by various agents have established how the forcings affect other climate variables besides temperature (e.g., precipitation). The forcing–response linkage as simulated by models, including the diversity in the spatial distribution of forcings by the different agents, has provided a practical demonstration of the effectiveness of agents in perturbing the radiative energy balance and causing climate changes. The significant advances over the past half century have established, with very high confidence, that the global-mean ERF due to human activity since preindustrial times is positive (the 2013 IPCC assessment gives a best estimate of 2.3 W m−2, with a range from 1.1 to 3.3 W m−2; 90% confidence interval). Further, except in the immediate aftermath of climatically significant volcanic eruptions, the net anthropogenic forcing dominates over natural radiative forcing mechanisms. Nevertheless, the substantial remaining uncertainty in the net anthropogenic ERF leads to large uncertainties in estimates of climate sensitivity from observations and in predicting future climate impacts. The uncertainty in the ERF arises principally from the incorporation of the rapid climate adjustments in the formulation, the well-recognized difficulties in characterizing the preindustrial state of the atmosphere, and the incomplete knowledge of the interactions of aerosols with clouds. This uncertainty impairs the quantitative evaluation of climate adaptation and mitigation pathways in the future. A grand challenge in Earth system science lies in continuing to sustain the relatively simple essence of the radiative forcing concept in a form similar to that originally devised, and at the same time improving the quantification of the forcing. This, in turn, demands an accurate, yet increasingly complex and comprehensive, accounting of the relevant processes in the climate system.


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