Rethinking the Holocene temperature conundrum

[1] A detailed overview of long-term secular trends in temperature of the mesosphere and lower thermosphere considered to be induced by increase in greenhouse gases has been provided by Beig et al. (2003). Since then, quite a few new results have been emerged as some of the data series have become sufficiently large enough to provide results with improved confidence. Our understanding on the nature of temperature trends in the mesosphere/lower thermosphere (MLT) region is relatively better now. In the mesosphere, some of the results confirmed the earlier findings, and some new results obtained by satellite and lidar data over the tropical region have indicated a relatively weaker cooling trend as compared to the past but nevertheless strengthened the conclusion about the cooling trends. However, in the mesopause region, some of the new results now indicate a break in trend and tendency of negative signal where earlier no trend feature was noticed. This slice of no trend feature in between two cooling regimes was puzzling the modeling community, who were in search of a convincing explanation. This paper briefly outlines the progress made over the recent past in the field of MLT region secular temperature trends attributed mainly to growth of greenhouse gases near the Earth's surface.

<p>Under continued high anthropogenic CO<sub>2</sub> emissions, the atmospheric CO<sub>2</sub> concentration around 2100 will be like that of the Early Eocene Climate Optimum (EECO, 56–48 Ma) hothouse period. Hence, reconstructions of the EECO climate give insight into the workings of the climate system under the possible future CO<sub>2</sub> conditions. Our current understanding of global mean surface temperature (GMST) during the Cenozoic era relies on paleo-proxy estimates of deep-sea temperature (DST) combined with assumed relationships between global mean DST (GMDST), global mean sea-surface temperature (GMSST), and GMST. The validity of these assumptions is essential in our understanding of past and future climate states under hothouse conditions.<br>We analyse the relationship between these global temperature indicators for the end-of-simulation global mean temperature values in 25 different millennia-long model simulations of the EECO climate under varying CO<sub>2</sub> levels, performed as part of the Deep-Time Model Intercomparison Project (DeepMIP). The model simulations show limited spatial variability in DST, indicating that local DST estimates can be regarded representative of GMDST. Linear regression analysis indicates that GMDST and GMST respond stronger to changes in atmospheric CO<sub>2</sub> than GMSST by factors 1.18 and 1.17, respectively. Consequently, the responses of GMDST and GMST to atmospheric CO<sub>2</sub> changes are similar in magnitude. This model-based analysis indicates that changes in GMDST can be used to estimate changes in GMST during the EECO, validating the assumed relationships. To test the robustness of these results, other Cenozoic climate states besides EECO should be analysed similarly.</p>

A large fraction of emissions of carbon dioxide (CO 2 )-the greenhouse gas produced by human activities in the greatest quantities-is long lived in the atmosphere, so decisions made today to continue adding CO 2 into the atmosphere may lock future generations into a range of human health and environmental impacts, some of which could become very severe, according to a committee of the National Research Council (NRC). In a report that looks at the short-and long-term effects of the stabilization of Earth's temperature, the NRC committee quantifies, as much as possible, the outcomes of different stabilization targets for the planet, with a focus on the United States. 1

IT has been suggested1–5 that solar variability may be a significant contributor to the long-term warming trend that has been observed in global mean surface air temperature since the mid-nineteenth century. Here we consider the role of long-term solar irradiance changes associated with sunspot fluctuations, extending an earlier analysis by Reid4. We find that solar variability is unlikely to have accounted for more than a small fraction of the observed warming.

Overshoot pathways involve exceeding a specific temperature target temporarily and returning to it using deliberate carbon dioxide removal methods. Quantifying the overshoot carbon budgets is becoming increasingly significant as the global mean surface air temperature approaches the 1.5°C target considered in the Paris Agreement. Contribution from non-CO2 forcings is a key component of estimating the carbon budgets. Non-CO2 forcings affect global mean temperature in two ways: i) by altering the energy balance at the top of the atmosphere (direct effect) and ii) by affecting the carbon cycle (indirect effect; for example, the effect of non-CO2 forcings on temperature causes changes in soil respiration which is a strong function of temperature). Current frameworks quantify the impact of non-CO2forcings on carbon budgets separately from CO2 forcing using emulators. Therefore, the effects of the interaction between non-CO2 forcings and carbon cycle (indirect effects) are not captured. Pre- and post-overshoot carbon budgets refer to the total anthropogenic emissions when the temperature exceeds and subsequently falls below the intended target, respectively. Here, we investigate how the indirect effects of non-CO2 forcings on global mean temperatures affect pre- and post-overshoot carbon budgets using an Earth system model of intermediate complexity.Three sets of simulations are performed to isolate the direct and indirect effects of non-CO2 forcings on global mean surface air temperatures. The reference set involves prescribing fossil fuel emissions following historical data and Shared Socio-economic Pathways (SSP) scenarios, while excluding non-CO2 forcings. The second set (total set) involves simulations with both fossil fuel emissions and non-CO2 forcings prescribed following historical data and SSP scenarios, which simulates the total effect of non-CO2 forcings on global mean temperature. In the third set (direct set), the same non-CO2 forcings as in the total set is applied, but the atmospheric CO2 concentration is prescribed from the reference simulation. Prescribing atmospheric CO2 concentration isolates the direct effects due to non-CO2 forcings by preventing the carbon cycle feedbacks from influencing temperature. The indirect effects are calculated as the difference between total and direct sets. We find that direct warming due to non-CO2 forcing is larger at both pre-and post-overshoots compared to indirect warming. However, the relative contribution of indirect warming increases during the post-overshoot relative to the pre-overshoot because of two reasons: i) non-CO2 forcings are smaller during the post-overshoot and ii) indirect warming increases from pre- to post-overshoot because of the slow carbon cycle response to non-CO2 warming. Further, we estimate the associated reductions in pre- and post-overshoot carbon budgets due to indirect effects of non-CO2 forcings. Our results suggest that frameworks quantifying overshoot carbon budgets should assess the contributions from CO2 and non-CO2 forcings together to fully capture the effects of the interactions between non-CO2 forcings and the carbon cycle.

Abstract. Ring-width (RW) records from the Gulf of Alaska (GOA) have yielded a valuable long-term perspective for North Pacific changes on decadal to longer timescales in prior studies but contain a broad winter to late summer seasonal climate response. Similar to the highly climate-sensitive maximum latewood density (MXD) proxy, the blue intensity (BI) parameter has recently been shown to correlate well with year-to-year warm-season temperatures for a number of sites at northern latitudes. Since BI records are much less labour intensive and expensive to generate than MXD, such data hold great potential value for future tree-ring studies in the GOA and other regions in mid- to high latitudes. Here we explore the potential for improving tree-ring-based reconstructions using combinations of RW- and BI-related parameters (latewood BI and delta BI) from an experimental subset of samples at eight mountain hemlock (Tsuga mertensiana) sites along the GOA. This is the first study for the hemlock genus using BI data. We find that using either inverted latewood BI (LWBinv) or delta BI (DB) can improve the amount of explained temperature variance by > 10 % compared to RW alone, although the optimal target season shrinks to June–September, which may have implications for studying ocean–atmosphere variability in the region. One challenge in building these BI records is that resin extraction did not remove colour differences between the heartwood and sapwood; thus, long term trend biases, expressed as relatively warm temperatures in the 18th century, were noted when using the LWBinv data. Using DB appeared to overcome these trend biases, resulting in a reconstruction expressing 18th–19th century temperatures ca. 0.5 °C cooler than the 20th–21st centuries. This cool period agrees well with previous dendroclimatic studies and the glacial advance record in the region. Continuing BI measurement in the GOA region must focus on sampling and measuring more trees per site (> 20) and compiling more sites to overcome site-specific factors affecting climate response and using subfossil material to extend the record. Although LWBinv captures the inter-annual climate signal more strongly than DB, DB appears to better capture long-term secular trends that agree with other proxy archives in the region. Great care is needed, however, when implementing different detrending options and more experimentation is necessary to assess the utility of DB for different conifer species around the Northern Hemisphere.

In recent years, the earth is burning, whether global warming and its influencing factors, based on time series and gray prediction model, put forward the hypothesis and conclusion, provide the data of various factors, to evaluate the past temperature level, to predict the future global temperature level and its trend and analyze the influencing factors. For problem 1, first, based on the analysis of the global average air temperature data from March 2012-February 2022 and March 2022-October 2022, it is unscientific that the global temperature rise in March 2022 led to a larger rise than in the past 10 years. Second, the ARIMA model and the gray prediction model are used to describe the past global temperature levels and predict the future global temperature levels, respectively. The ARIMA model and the grey prediction model show that the global average annual temperature change has been stable in the past, while the prediction that the average temperature of 20.00°C in 2050 or 2100 is inaccurate. The grey prediction model shows that the global temperature levels will continue to rise in the future, and shows that the average temperature in 2,289 observations is expected to reach 20.00°C. According to the forecast, the global average temperature will not, as long as natural resources are properly used, reach 20.00°C. For problem 2, We first divide the Earth into the southern and northern hemisphere, and second into tropical and southern temperate and northern temperate zones. The linear relationship between global average temperature and the two conditions is discussed separately. According to the results, it significantly shows a strong correlation between the global average temperature and time. If unchecked, the global average temperature will grow over time. Second, using the principal component analysis method shows that may affect the global average temperature in the Middle East energy consumption, global hydropower consumption, world oil consumption, global niobium minerals, the world gas proven reserves, global crude oil production, global cadmium production, and to the results are tested, concluded that the principal component analysis method to influence the main indicators of the global average temperature method is more accurate. Therefore, the suggestions for global temperature change are made: to strive to reduce methane emissions; to further strengthen the strategic deployment and practical actions for energy revolution and low-carbon development, while increasing the intensity of energy low-carbon transformation. For problem 3, We propose the feasibility of this model evaluation and prediction results on slowing down or curbing global warming trends. One is direct emissions reduction, with countries signing contracts to force their carbon emissions and unite against global warming. Reduce heat emissions should be put on everyone's daily living habits, with a limited use of daily necessities, to achieve limited production, limited processing, so as to curb industrial flooding, reduce heat emissions. Second, the indirect emission reduction. With the development of industry, carbon dioxide emissions are constantly increasing, leading to a series of problems such as global warming and sea level rise. Afforestation can absorb the carbon dioxide contained in the atmosphere and reduce the greenhouse gas content in the atmosphere. Therefore, we must realize the importance of afforestation, take appropriate measures to improve the effect of afforestation, and better alleviate global warming.

<strong class="journal-contentHeaderColor">Abstract.</strong> Single model initial-condition large ensembles (LEs) are a useful approach to understand roles of forced responses and internal variability in historical and future climate change. Here, we produce one of the largest ensembles thus far using the MIROC6 coupled atmosphere-ocean global climate model (MIROC6-LE). The total experimental period of MIROC6-LE is longer than 76000 years. MIROC6-LE consists of a long preindustrial control run, 50-member historical simulations, 8 single forcing historical experiments with 10 or 50 members, 5 future scenario experiments with 50 members and 3 single forcing future experiments with 50 members. Here, we describe the experimental design. The output data of most of the experiments are freely available to the public. This dataset would be useful to a wide range of research communities. We also demonstrate some examples of initial analyses. Specifically, we confirm that the linear additivity of the forcing-response relationship holds for the 1850&ndash;2020 trends of the annual mean values and extreme indices of surface air temperature and precipitation by analyzing historical fully forced runs and the sum of single forced historical runs. To isolate historical anthropogenic signals of annual mean and extreme temperature for 2000&ndash;2020 relative to 1850&ndash;1900, ensemble sizes of 4 and 15, respectively, are sufficient in most of the world. Historical anthropogenic signals of annual mean and extreme precipitation are significant with the 50-member ensembles in 76 % and 69 % of the world, respectively. Fourteen members are sufficient to examine differences in changes in annual mean values and extreme indices of temperature and precipitation between the shared socioeconomic pathways (ssp), ssp585 and ssp126, in most of the world. Ensembles larger than 50 members are desirable for investigations of differences in annual mean and extreme precipitation changes between ssp126 and ssp119. Historical and future changes in internal variability, represented by departures from the ensemble mean, are analyzed with a focus on the El Ni&ntilde;o/Southern Oscillation (ENSO) and global annual mean temperature and precipitation. An ensemble size of 31 is large enough to detect ENSO intensification from preindustrial conditions to 1951&ndash;2000, from 1951&ndash;2000 to 2051&ndash;2100 in all future experiments, and from low- to high-emission future scenario experiments. The single forcing historical experiments with 27 members can isolate ENSO intensification due to anthropogenic greenhouse gas and aerosol forcings. Future changes in the global mean temperature variability are discernible with 23 members under all future experiments, while 50 members are not sufficient for detecting changes in the global mean precipitation variability in ssp119 and ssp126. We also confirm that these temperature and precipitation variabilities are not precisely analyzed when detrended anomalies from the long-term averages are used due to interannual climate responses to the historical natural forcing, which highlights the importance of large ensembles for assessing internal variability.

In accordance with record high global mean temperatures new local maximum temperature records have been observed in many places worldwide over the last few years. The question arises, what is the highest 2-meter temperature on earth to be expected in the near future due to global warming? The investigation focuses on the temperature time series of Death Valley, California, the present world record holder of maximum temperature. A critical review is given on this maximum temperature world record of 134 °F or 56.7 °C, set on 10 July 1913. Different evaluations like comparison with neighbor stations, comparison with 20th century re-analyses and measures of dispersion are carried out. They all show that this record is highly questionable, because no physical mechanism is known, which could explain such a statistical outlier of the maximum temperature on a local scale over desert regions. A new low-pass filter technique of time series is being used to determine the long-term climatological temperature trend between 1911 and 2023 in Death Valley in the frame of global change, which is quite impressive. Finally, the probabilities of the occurrence of certain temperature thresholds in the near future are derived by utilizing a general extreme value distribution. It is shown that the probability of a new temperature world record of 135 °F or 57.2 °C in the next few years, which would make the present—albeit questionable—record obsolete, is still very low, despite global warming.

High-quality monthly upper-air temperature and humidity datasets of 22 stations have been developed for monitoring and assessing long-term trends in temperature and humidity over Australia. In addition, high quality monthly upper-air temperature and humidity time series have been developed for five Australian remote island sites: the subantarctic Macquarie Island, Lord Howe Island in the Tasman Sea, Norfolk Island in the southwest Pacific Ocean, Willis Island in the Coral Sea and Cocos Island in the eastern Indian Ocean. The datasets are based on 23UTC soundings with most records starting in 1958 for temperature and in 1965 for humidity. The quality control process involved the examination of station metadata (historical information about stations and recordings) and an objective statistical test which detected discontinuities in the data series. At each station and each level, data series were adjusted (on a monthly timescale) when discontinuities were identified. Homogenised temperature and humidity (represented by the dew point temperature, DWPT) data were analysed at mandatory levels. Temperature time series were found to be mostly complete since 1958, with higher levels having more missing data than lower levels. For humidity it was found that there were insufficient daily observations to compute monthly means in the period 1965 to 1991 at all levels except 850 hPa. Furthermore, DWPT data contained a number of biases due to changes in technology. To be able to analyse humidity data at least at the two lowest levels (850 and 700 hPa), we explored the possibility of removing part of the daily data, in order to make the record over the period 1965-2011 consistent (i.e. independent of technological changes). The all-Australian mean annual temperature shows statistically significant increases/decreases over the 1958-2011 period at all examined levels except the 150 hPa level. Since 1958 temperatures at the 850 and 700 hPa levels increased about 1.2°C, while at the 500, 400 and 300 hPa levels they increased about 1.6°C. At levels which intercept the tropopause height, temperature over the same period of time increased by 0.9°C at the 200 hPa and a decreased by -0.3°C at the 150 hPa. In the lower stratosphere, at the 100 hPa level, temperature decreased by -1.8°C. A similar vertical structure of changes in temperature is found in remote islands data. The observed patterns of change in the upper-air temperature over Australia are in agreement with physical principles and model simulations under the enhanced greenhouse effect, both of which show general warming in the troposphere and cooling in the lower stratosphere (noting that stratospheric cooling is predominantly due to the decrease in stratospheric ozone and to a smaller extent to the increase of greenhouse gases). Results are also consistent with the expectation that the troposphere should warm at a faster rate than the surface. Analysis of the homogenised humidity data indicates that over the 1965-2011 period, all-Australian DWPT shows statistically significant increases at the 850 and 700 hPa levels. The increase since 1965 at 850 hPa is about 0.9°C (comparable to the temperature tend), while at 700 hPa the increase in DWPT is nearly double in magnitude, about 1.rc (larger than the temperature trend). The increase in humidity in the lower troposphere is in accordance with the expectation that, as the troposphere warms, the amount of moisture in it should increase. It is also consistent with an upward trend in observed Australian near-surface humidity.

We examine an oscillation of global mean temperature with a period of about two thirds of a century. We find evidence for the oscillation both in the instrumental temperature record and in an Earth System Model millennium simulation without external forcing. There is also evidence for the oscillation in the Central England Temperature record, the longest instrumental record available. Our method is based on a discrete Fourier transform with varying starting point and length of time window. This method allows us to make a quantitative estimate of the contribution of an oscillation to global mean temperature, to track the phase evolution of the oscillation and to compare measurement and model results. The multidecadal oscillation could provide part of the explanation both for near-constant global mean temperatures in recent years despite warming by rising concentrations of greenhouse gases and for declining global mean temperature in the 1950s and 1960s alongside with the explanation of aerosol cooling. Quantitative estimates of the contribution of the oscillation to global mean temperature vary between ±(0.03–0.17) K. For the instrumental temperature record, our results indicate an amplitude of 0.03 K presently if the IPCC model average represents the effect of external forcings well, and (0.08–0.17) K when using simple linear and quadratic fits for detrending. For the millennium simulation, the amplitude of the oscillation is (0.05–0.06) K, but could be underestimated as compared to reality if external forcing acts to globally synchronize multidecadal variability. The role of the Atlantic Multidecadal Oscillation (AMO) in the model is discussed. The AMO has a spatial temperature distribution similar to earlier literature results and is more correlated with the global oscillation when external forcing is included.

Orbitally driven evolution of Asian monsoon and stable water isotope ratios during the Holocene: Isotope-enabled climate model simulations and proxy data comparisons

The global tropospheric temperature hit an all-time maximum in 2023. While new global average temperature records are routinely set as a result of greenhouse warming, the year-to-year rise in temperature from 2022 to 2023 is infrequently reproduced in climate models (e.g., Raghuraman et al. 2024). It is possible that such a rise in temperature could result from a unique manifestation of internal climate variability (e.g., Cattiaux et al. 2023). Earth recently experienced a transition from a prolonged La Ni&amp;#241;a event to an El Ni&amp;#241;o event. This phasing of ENSO variability is associated with increased odds of a spike in global temperature in climate models (e.g., Raghuraman et al. 2024). Other analyses indicate that multi-year trends in low cloud cover, aerosols, and the peak of the 11-year solar cycle are also important factors (e.g., Goessling et al. 2024). Now into 2025, we find that the year-to-year rise in temperature (over 2023 to 2024) is again large and the annual mean global tropospheric temperature anomaly (in 2024) exceeds the record set in the previous year. We examine the rapid and persistent rise in global tropospheric temperature using microwave-based measurements of tropospheric temperature from satellites. We contextualize the exceptional 2023-2024 warmth using model simulations of the a) pre-industrial period and b) the satellite era in order to estimate the effects of internal variability and greenhouse warming. We also explore the sensitivity of our results to model climate sensitivity and the representation of interannual variability with data from several large initial condition ensembles.Cattiaux, J., Ribes, A., &amp;amp; Cariou, E. (2024). How extreme were daily global temperatures in 2023 and early 2024? Geophysical Research Letters, 51(19). https://doi.org/10.1029/2024gl110531Goessling, H. F., Rackow, T., &amp;amp; Jung, T. (2024). Recent global temperature surge intensified by record-low planetary albedo. Science. https://doi.org/10.1126/science.adq7280Raghuraman, S. P., Soden, B., Clement, A., Vecchi, G., Menemenlis, S., &amp;amp; Yang, W. (2024). The 2023 global warming spike was driven by the El Ni&amp;#241;o&amp;#8211;Southern Oscillation. Atmospheric Chemistry and Physics, 24(19), 11275&amp;#8211;11283. https://doi.org/10.5194/acp-24-11275-2024Research at Lawrence Livermore National Laboratory was performed under the auspices of U.S. DOE Contract DE-AC52-07NA27344. This research was performed as part of the PCMDI Project, which is funded by the RGMA program area of the Office of Science at DOE.

Estimating global impacts from climate change

This study presents the characterization of regional means and variability of temperature and precipitation in 1961–2000 for Thailand using regional climate model RegCM3. Two fine-resolution (20 km) simulations forced by ERA-40 reanalysis data were performed, with the default land covers and with a land-cover modification strategy suggested by a previous work. The strategy was shown to substantially alleviate the problem of systematic underestimation of temperature given by the default simulation, for most part of Thailand in both dry and wet seasons. The degree of bias in precipitation tends to vary differently in every sub-region and season considered. The patterns of seasonal variation of both climatic variables are acceptably reproduced. Simulated 850-hPa winds have general agreement with those of ERA-40, but wind speed is overestimated over the Gulf of Thailand during the dry months, potentially bringing excessive moisture to and causing more rain than actual in the south. Long-term trends in temperature are reasonably predicted by the model while those in observed and simulated precipitations for upper Thailand are in the opposite directions. Apart from the conventional methods used in characterization, spectral decomposition using Kolmogorov–Zurbenko filters was applied to inspect the model’s capability of accounting for variability (here, in terms of variance) in both climatic variables on three temporal scales (short term, seasonal, and long term). The model was found to closely estimate the total variances in the original time series and fairly predict the relative variance contributions on all temporal scales. The latter finding is in line with the results from an additional spectral coherence analysis. Overall, the model was shown to be acceptably adequate for use in support of further climate studies for Thailand, and its evident strength is the capability of reproducing seasonal characteristics and, to a lesser degree, trends.