Importance of beginning industrial-era climate simulations in the eighteenth century

The Australian Community Climate and Earth System Simulator (ACCESS) coupled climate model version 1.3 participated in phase five of the Coupled Model Intercomparison Project (CMIP5) with an initial contribution of high priority experiments. Further standard experiments have since been conducted with ACCESS1.3, including an ensemble of three simulations for the historical period (1850-2005) forced with time-evolving natural and anthropogenic forcings. Additional ensembles of simulations have been conducted for the same period with subsets of known forcings, including with natural forcings only ('historicalNat') and with greenhouse gas forcings only ('historicalGHG'). In this study, we describe this ACCESS1.3 contribution to CMIP5 and assess several key aspects of ACCESS1.3 forced responses in these experiments against observations and an ensemble of participating CMIP5 models, consisting of 40 realisations from ten models. Overall, ACCESS1.3 historical experiments demonstrate skill in simulating the global and regional metrics assessed that is comparable to the CMIP5 multi-model ensemble utilised. Global annual average temperature and precipitation trends simulated with ACCESS1.3 (0.05-0.07 K/decade; -0.007 to -0.0004 mm day /decade) largely lie within the CMIP5 ensemble window (0.06 - 0.18 K/decade; -0.01 to 0.009 mm day /decade) and near those observed (0.10 K/decade; -0.0007 to -0.001 mm day-1/decade) over the 1950-2005 period. For the ACCESS1.3 historicalNat and historicalGHG experiments, simulated temperature trends are also predominately within the CMIP5 multi-model ensemble range. Similarly, ACCESS1.3 (-0.07 to -0.12 K) and the CMIP5 models (-0.03 to -0.21 K) largely capture the composited observed decrease in global temperature (-0.04 K) following three major late 20th century volcanic eruptions. However, like all global climate models, ACCESS1.3 has deficiencies that should be considered. In particular, one of most notable features of ACCESS1.3 historical simulations is the reduced warming trend over the period 1950-2005 that is evident in all ACCESS1.3 realisations at the global-scale for Australia, relative to both observations and the CMIP5 multi-model mean. This appears to be related to the overly strong response to increases in anthropogenic aerosols. Overall, these historical period experiments using ACCESS1.3 with various forcings are useful for inclusion with other CMIP5 models for studies aimed at detecting and attributing climatic changes. -1 -1

Understanding and quantifying natural climate variability is a prerequisite to detect and attribute anthropogenic warming and to project future climate change. It is important to extend the evaluation of models used for climate projections through the pre-industrial period when natural variations were pronounced while anthropogenic influence was small. In anticipating future climate change, there are three main sources of uncertainty. 1) We do not know the future anthropogenic emissions and resulting atmospheric concentrations of greenhouse gases and aerosols. 2) The response to greenhouse gas and aerosol forcing differs between various models, simulated regional climate changes being particularly model-dependent. 3) In addition to anthropogenic forcing, climate changes are induced by natural forcing (e.g., volcanoes and variations in solar activity) as well as by unforced internal variability in the climate system. The tools most commonly adopted for projecting future climate are coupled atmosphere-ocean general circulation models (AOGCMs). These numerical models provide a comprehensive three-dimensional representation of the climate system, describing the main dynamical and physical processes, their interactions and feedbacks. They can generate regional estimates of climate in response to given changes in greenhouse gas and aerosol concentrations. The four main relevant forcings (greenhouse gases, solar variability, volcanism, land-use change) have different time-dependence over long periods, so can be separated more effectively than for the shorter instrumental period. The potential role of solar variations in modulating recent climate has been debated for many decades and recent papers suggest that solar forcing may be less than previously believed. Century-scale solar irradiance variations have been proposed as cause for past climatic changes. However, recently, astronomical evidence has been used to suggest that low-frequency variability of solar irradiance might be very low, possibly restricted to the range of the observed high-frequency variability. We used a climate model to analyze past climatic responses to solar and volcanic forcing, using a solar irradiance history partially based on a recent 10Be findings from Antarctica. Our results suggest that, while solar irradiance changes and volcanism were the dominant forcings in preindustrial times, their combined role has been changing over the past century. Although these natural forcing factors could be responsible for some modification of the decadal structure over the 20th century, they only played a minor role in the most recent warming. Therefore, the 20th century warming is not a reflection of a rebound from the last Little Ice Age cool period, but it is largely caused by anthropogenic forcing. A small role of solar forcing for late 20th century climate change is additionally supported by the absence of a trend in the satellite-based irradiance record covering the past 30 years. In conclusion, our model results indicate that the range of Northern-Hemisphere temperature reconstructions and natural forcing histories (cosmogenic isotope record as a proxy for solar forcing, and volcanic forcing) constrain the natural contribution to 20th century warming to be +0.2°C. Anthropogenic forcing must account for the difference between the small natural forcings and the observed warming in the late 20th century

Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network

Attribution of extreme climate events to global climate change as a result of anthropogenic greenhouse gas emissions has become increasingly important. Extreme climate events arise at the intersection of natural climate variability and a forced response of the Earth system to anthropogenic greenhouse gas emissions, which may alter the frequency and severity of such events. Accounting for the effects of both natural climate variability and the forced response to anthropogenic climate change is thus central for the attribution. Here, we investigate the reproducibility of probabilistic extreme event attribution results under more explicit representations of natural climate variability. We employ well-established methodologies deployed in statistical Earth System Model emulators to represent natural climate variability as informed from its spatio-temporal covariance structures. Two approaches towards representing natural climate variability are investigated: (1) where natural climate variability is treated as a single component; and (2) where natural climate variability is disentangled into its annual and seasonal components. We showcase our approaches by attributing the 2022 Indo-Pakistani heatwave to human-induced climate change. We find that explicit representation of annual and seasonal natural climate variability increases the overall uncertainty in attribution results considerably compared to established approaches such as the World Weather Attribution Initiative. The increase in likelihood of such an event occurring as a result of global warming differs slightly between the approaches, mainly due to different assessments of the pre-industrial return periods. Our approach that explicitly resolves annual and seasonal natural climate variability indicates a median increase in likelihood by a factor of 41 (95% range: 6-603). We find a robust signal of increased likelihood and intensification of the event with increasing global warming levels across all approaches. Compared to its present likelihood, under 1.5 °C (2 °C) of global near-surface air temperature increase relative to pre-industrial temperatures, the likelihood of the event would be between 2.2 to 2.5 times (8 to 9 times) higher. We note that regardless of the different statistical approaches to represent natural variability, the outcomes on the conducted event attribution are similar, with minor differences mainly in the uncertainty ranges. Possible reasons for differences are evaluated, including limitations of the proposed approach for this type of application, as well as the specific aspects in which it can provide complementary information to established approaches.

’s (2012) conclusion that observed climate change is comparableto projections, and in some cases exceeds projections, allows further inferences ifwe can quantify changing climate forcings and compare those with projections.The largest climate forcing is caused by well-mixed long-lived greenhouse gases.Here we illustrate trends of these gases and their climate forcings, and we discussimplications. We focus on quantities that are accurately measured, and we includecomparison with fixed scenarios, which helps reduce common misimpressionsabout how climate forcings are changing.Annual fossil fuel CO

We obtained estimates of the relationship of changes in the global surface air temperature (GSAT) with different natural and anthropogenic factors based on empirical data beginning from the middle of the 19th century using the Granger causality test estima� tion and application of cross wavelet analysis. Along with the solar and volcanic activity and changes of the carbon dioxide concentration in the atmosphere, we estimated the role of quasicyclic processes in the Earth's climatic system. We analyzed the climatic vari� ations detected by the index of the Atlantic multidec� adal oscillation (AMO) with a characteristic period of approximately 60-70 years and the variations in the angular velocity of the Earth. We made a conclusion on the basis of the empirical regression models based on data beginning from the middle of the 19th century that the changes of the CO2 concentration in the atmosphere have a determining influence on the longterm (secular) GSAT trends. The natural climatic cycles with periods of a few decades influence significantly only on the relatively fast GSAT variations. The influence of natural factors related to solar and volcanic activity on the longterm trends appeared to be much less significant. One of the modern key problems is estimating the role of natural and anthropogenic factors of global cli� mate changes. The natural climatic variability not related to external forcing is characterized by a wide spectrum of temporal and spatial scales and the effects of an anthropogenic character can hardly be distin� guished against the background of the natural variabil� ity. The problem of distinguishing the anthropogenic influence is strongly complicated by the effects of non� linearity and stochasticity in the climatic system under

To mitigate climate change at local, regional, and global scales, we must begin to think beyond greenhouse gases.

Typically, climate simulations covering the historical period start in 1850, with the first fifty years used as a baseline to represent a ‘pre-industrial' climate. The period immediately prior to 1850 is however of particular interest, as it had far more volcanic activity than any time during the subsequent historical period, and this is known to have caused large cooling of global temperatures. Exploring the climate of this period could help to better understand early anthropogenic warming, natural climate variability and anticipate the response to large future eruptions.Here we will: (1) highlight the development of a new instrumental observation-based dataset (GloSAT) for temperature variations across the globe from 1781 to present; (2) discuss an ensemble of historical simulations with UKESM1 which were started in 1750, 100 years earlier than typical. These two sources of evidence will be used to identify the long-lasting impacts of the early 19th century volcanism and disentangle it from the response to other forcings and internal variations. Longer term effects of this period are also explored with significant differences found with historical simulations run using the same model initialised in 1850 lasting well into the 20th century. The implications of this discrepancy and the role of large volcanic eruptions on multi-decadal climate will be discussed. 

A strategy using statistically optimal fingerprints to detect anthropogenic climate change is outlined and applied to near-surface temperature trends. The components of this strategy include observations, information about natural climate variability, and a “guess pattern” representing the expected time–space pattern of anthropogenic climate change. The expected anthropogenic climate change is identified through projection of the observations onto an appropriate optimal fingerprint, yielding a scalar-detection variable. The statistically optimal fingerprint is obtained by weighting the components of the guess pattern (truncated to some small-dimensional space) toward low-noise directions. The null hypothesis that the observed climate change is part of natural climate variability is then tested. This strategy is applied to detecting a greenhouse-gas-induced climate change in the spatial pattern of near-surface temperature trends defined for time intervals of 15–30 years. The expected pattern of climate change is derived from a transient simulation with a coupled ocean-atmosphere general circulation model. Global gridded near-surface temperature observations are used to represent the observed climate change. Information on the natural variability needed to establish the statistics of the detection variable is extracted from long control simulations of coupled ocean-atmosphere models and, additionally, from the observations themselves (from which an estimated greenhouse warming signal has been removed). While the model control simulations contain only variability caused by the internal dynamics of the atmosphere-ocean system, the observations additionally contain the response to various external forcings (e.g., volcanic eruptions, changes in solar radiation, and residual anthropogenic forcing). The resulting estimate of climate noise has large uncertainties but is qualitatively the best the authors can presently offer. The null hypothesis that the latest observed 20-yr and 30-yr trend of near-surface temperature (ending in 1994) is part of natural variability is rejected with a risk of less than 2.5% to 5% (the 5% level is derived from the variability of one model control simulation dominated by a questionable extreme event). In other words, the probability that the warming is due to our estimated natural variability is less than 2.5% to 5%. The increase in the signal-to-noise ratio by optimization of the fingerprint is of the order of 10%–30% in most cases. The predicted signals are dominated by the global mean component; the pattern correlation excluding the global mean is positive but not very high. Both the evolution of the detection variable and also the pattern correlation results are consistent with the model prediction for greenhouse-gas-induced climate change. However, in order to attribute the observed warming uniquely to anthropogenic greenhouse gas forcing, more information on the climate's response to other forcing mechanisms (e.g., changes in solar radiation, volcanic, or anthropogenic sulfate aerosols) and their interaction is needed. It is concluded that a statistically significant externally induced warming has been observed, but our caveat that the estimate of the internal climate variability is still uncertain is emphasized.

Global average surface air temperature can change when it is either ‘forced’ to change by factors such as increasing greenhouse gasses, or it can change on its own through ‘unforced’ natural cycles like El-Niño/La-Niña. In this paper we estimated the magnitude of unforced temperature variability using historical datasets rather than the more commonly used computer climate models. We used data recorded by thermometers back to the year 1880 as well as data from “nature’s thermometers” – things like tree rings, corals, and lake sediments – that give us clues of how temperature varied naturally from the year 1000 to 1850. We found that unforced natural temperature variability is large enough to have been responsible for the decade-to-decade changes in the rate of global warming seen over the 20th century. However, the total warming over the 20th century cannot be explained by unforced variability alone and it would not have been possible without the human-caused increase in greenhouse gasses. We also found that unforced temperature variability may be the driver behind the reduced rate of global warming experienced at the beginning of the 21st century.

The main results of a transient climate simulation of the last 500 years with a coupled atmosphere-ocean model driven by estimated solar variability, volcanic activity and atmospheric concentrations of greenhouse gases are presented and compared with several empirical climate reconstructions. Along the last five centuries the climate model simulates a climate colder than mean 20th century conditions almost globally, and the degree of cooling is clearly larger than in most empirical reconstructions of global and North hemispheric near-surface air temperature (MANN et al., 1998; JONES et al., 1998). The simulated temperatures tend to agree more closely with the reconstruction of ESPER et al. (2002) based on extratropical tree-ring chronologies. The model simulates two clear minima of the global mean temperature around 1700 A.D. (the Late Maunder Minimum) and around 1820 A.D. (the Dalton Minimum). The temperature trends simulated after the recovery from these minima are as large as the observed warming in the 20th century. More detailed results concerning the simulated Late Maunder Minimum, together with a spatially resolved historical reconstruction of the temperature field in Europe, are presented. It is found that the broad patterns of temperature deviations are well captured by the model, with stronger cooling in Central and Eastern Europe and weaker cooling along the Atlantic coast. However, the model simulates an intense drop of air-temperature in the North Atlantic ocean, together with an extensive sea-ice cover south of Greenland and lower salinity in North Atlantic at high latitudes, reminiscent of the Great Salinity Anomaly. Also, during the Late Maunder Minimum the intensities of the Golf Stream and the Kuroshio are reduced. This weakening is consistent with a reduced wind-stress forcing upon the ocean surface.

Knowledge of natural climate variability is essential to better constrain the uncertainties in projections of twenty-first-century climate change 1–5. The past 2,000 years (2 kyr) have emerged as a critical interval in this endeavour, with sufficient length to characterize natural decadal-to-centennial scale change, known external climate forcings 6 and with distinctive patterns of spatiotemporal temperature variations 7. However, reconstructions for the full 2 kyr interval are not available for the global ocean, a primary heat reservoir 8 and an important regulator of global climate on longer timescales 9–11. Here we present a global ocean sea surface temperature (SST) synthesis (Ocean2k SST synthesis) spanning the Common Era, which shows a cooling trend that is similar, within uncertainty, to that simulated by realistically forced climate models for the past millennium. We use the simulations to identify the climate forcing(s) consistent with reconstructed SST variations during the past millennium. The oceans mediate the response of global climate to natural and anthropogenic forcings. Yet for the past 2,000 years — a key interval for understanding the present and future climate response to these forcings — global sea surface temperature changes and the underlying driving mechanisms are poorly constrained. Here we present a global synthesis of sea surface temperatures for the Common Era (ce) derived from 57 individual marine reconstructions that meet strict quality control criteria. We observe a cooling trend from 1 to 1800 ce that is robust against explicit tests for potential biases in the reconstructions. Between 801 and 1800 ce, the surface cooling trend is qualitatively consistent with an independent synthesis of terrestrial temperature reconstructions, and with a sea surface temperature composite derived from an ensemble of climate model simulations using best estimates of past external radiative forcings. Climate simulations using single and cumulative forcings suggest that the ocean surface cooling trend from 801 to 1800 ce is not primarily a response to orbital forcing but arises from a high frequency of explosive volcanism. Our results show that repeated clusters of volcanic eruptions can induce a net negative radiative forcing that results in a centennial and global scale cooling trend via a decline in mixed-layer oceanic heat content.

The calculation of global land surface air temperature trends using the instrumental record has been based primarily upon two methods of maximizing the availability of station records. Hansen and Lebedeff[l981] developed a technique that is still used today, known as the reference station method; Jones et al. [1986a] popularized the climate anomaly method in their calculations of global temperature trends. In this paper we introduce yet another approach designed to maximize station records, referred to as the first difference method. To test the sensitivity of global temperature trend analysis to the method used, we calculate worldwide‐averaged land surface mean temperature using each of these methods with an identical data base, the Global Historical Climatology Network. For further comparisons, a global climate model (GCM) transient model simulation is interpolated to the Global Historical Climatology Network station locations and the three techniques are then applied to data interpolated to the station locations from the model. The Intergovernmental Panel on Climate Change (IPCC); [Nicholls et al. 1996] estimated a global land and ocean temperature change of 0.45°C±0.15°C since the 19th century. Their assessment of the uncertainty associated with this temperature trend did not specifically address the differences that the method of calculating a global temperature time series might produce. Our results indicate that the differences in 1880–1990 trends produced by these three different methods are only a few hundredths of a degree centigrade per 100 years on trends of approximately 0.5°C/100 years. This is quite small compared to the 0.15°C/100 years uncertainty associated with the IPCC global land and ocean assessment which included factors such as data homogeneity which are not addressed here. Indeed, our results indicate that the source of differences in trends is more likely to be the method used to calculate a linear trend from a global temperature time series than the method used to create the global temperature time series. The modeled results confirm this finding but highlight other important characteristics: the reference station method has uncharacteristically low interannual variance, more similar to time series from the entire globe (land and ocean) than the global land area from which the data were observed. This lower variance can impact the statistical significance associated with linear trends.

The effect of ocean mixed layer depth on climate is explored in a suite of slab ocean aquaplanet simulations with different mixed layer depths ranging from a globally uniform value of 50–2.4 m. In addition to the expected increase in the amplitude of the seasonal cycle in temperature with decreasing ocean mixed layer depth, the simulated climates differ in several less intuitive ways including fundamental changes in the annual mean climate. The phase of seasonal cycle in temperature differs non-monotonically with increasing ocean mixed layer depth, reaching a maximum in the 12 m slab depth simulation. This result is a consequence of the change in the source of the seasonal heating of the atmosphere across the suite of simulations. In the shallow ocean runs, the seasonal heating of the atmosphere is dominated by the surface energy fluxes whereas the seasonal heating is dominated by direct shortwave absorption within the atmospheric column in the deep ocean runs. The surface fluxes are increasingly lagged with respect to the insolation as the ocean deepens which accounts for the increase in phase lag from the shallow to mid-depth runs. The direct shortwave absorption is in phase with insolation, and thus the total heating comes back in phase with the insolation as the ocean deepens more and the direct shortwave absorption dominates the seasonal heating of the atmosphere. The intertropical convergence zone follows the seasonally varying insolation and maximum sea surface temperatures into the summer hemisphere in the shallow ocean runs whereas it stays fairly close to the equator in the deep ocean runs. As a consequence, the tropical precipitation and region of high planetary albedo is spread more broadly across the low latitudes in the shallow runs, resulting in an apparent expansion of the tropics relative to the deep ocean runs. As a result, the global and annual mean planetary albedo is substantially (20 %) higher in the shallow ocean simulations which results in a colder (7C) global and annual mean surface temperature. The increased tropical planetary albedo in the shallow ocean simulations also results in a decreased equator-to-pole gradient in absorbed shortwave radiation and drives a severely reduced (≈50 %) meridional energy transport relative to the deep ocean runs. As a result, the atmospheric eddies are weakened and shifted poleward (away from the high albedo tropics) and the eddy driven jet is also reduced and shifted poleward by 15° relative to the deep ocean run.

Despite many studies on reconstructing the climate changes over the last millennium in China, the cause of the China’s climate change remains unclear. We used the UVic Earth System Climate Model (UVic Model), an Earth system model of intermediate complexity, to investigate the contributions of climate forcings (e.g. solar insolation variability, anomalous volcanic aerosols, greenhouse gas, solar orbital change, land cover changes, and anthropogenic sulfate aerosols) to surface air temperature over East China in the past millennium. The simulation of the UVic Model could reproduce the three main characteristic periods (e.g. the Medieval Warm Period (MWP), the Little Ice Age (LIA), and the 20th Century Warming Period (20CWP)) of the northern hemisphere and East China, which were consistent with the corresponding reconstructed air temperatures at century scales. The simulation result reflected that the air temperature anomalies of East China were larger than those of the global air temperature during the MWP and the first half of 20CWP and were lower than those during the LIA. The surface air temperature of East China over the past millennium has been divided into three periods in the MWP, four in the LIA, and one in the 20CWP. The MWP of East China was caused primarily by solar insolation and secondarily by volcanic aerosols. The variation of the LIA was dominated by the individual sizes of the contribution of solar insolation variability, greenhouse gas, and volcano aerosols. Greenhouse gas and volcano aerosols were the main forcings of the third and fourth periods of the LIA, respectively. We examined the nonlinear responses among the natural and anthropogenic forcings in terms of surface air temperature over East China. The nonlinear responses between the solar orbit change and anomalous volcano aerosols and those between the greenhouse gases and land cover change (or anthropogenic sulfate aerosols) all contributed approximately 0.2°C by the end of 20th century. However, the output of the energy-moisture balance atmospheric model from UVic showed no obvious nonlinear responses between anthropogenic and natural forcings. The nonlinear responses among all the climate forcings (both anthropogenic and natural forcings) contributed to a temperature increase of approximately 0.27°C at the end of the 20th century, accounting for approximately half of the warming during this period; the remainder was due to the climate forcings themselves.