Global Mean Surface Temperature Research Articles

Climate Sensitivity and Ecoclimate Sensitivity: Theory, Usage, and Past Implications for Future Biospheric Responses

Two usages of ‘climate sensitivity’ co-exist: one climatological and one ecological. The earlier climatological usage quantifies the sensitivity of global mean surface temperature to atmospheric CO2, with formal variants differing by timescale and processes. The ecological usage, renamed here as ecoclimate sensitivity, is defined as a change in an ecological response variable per unit climate change. The two concepts are treated very differently: climatologists have focused on reducing uncertainty of global climate sensitivity estimates, while ecologists have focused on the multivariate processes governing variations in ecoclimate sensitivity across drivers, response variables, and scales. Because radiative forcing scales logarithmically to [CO2]atm, ecological impacts per ppm [CO2]atm often also scale logarithmically, although non-linear ecoclimate sensitivities can alter this expectation. Critically, past estimates of climate and ecoclimate sensitivity carry an implicit tradeoff, in which smaller estimates of climate sensitivity indicate higher ecoclimate sensitivities. For the LGM, estimates of equilibrium climate sensitivity have narrowed to 2.4 to 4.5 °C, while high ecoclimate sensitivity is indicated by post-glacial biome conversions, continental-scale species range shifts, and high community turnover. We introduce a new term, ecocarbon sensitivity, defined as the product of global climate sensitivity, local ecoclimate sensitivity, and a global-to-local climate scaling factor. Given past biospheric transformations, we can expect high sensitivity of the terrestrial biosphere to current rises in [CO2]atm, a conclusion that is insensitive to estimates of climate sensitivity. The next frontier is better quantification of the processes governing the form and variations of ecoclimate and ecocarbon sensitivity across systems and scales.

Changes in MJO Characteristics and Impacts in the Past Century

Abstract The changes of the MJO behavior and its impact on global precipitation, precipitation extremes, and high-frequency variability during the past century (1901–2010) were investigated using the NOAA-20CR dataset. It was found that the MJO amplitude was significantly strengthened, while its eastward phase speed hardly changed. The impacts of the MJO on precipitation in South China (SC), northern Australia (AU), and California (CA) were investigated. The anomalous rainfall in the regions was strengthened from the early to late twentieth century, with the percentage increase ranging from 67% in AU to 14% in CA. A moisture budget analysis indicated that the enhanced precipitation was primarily attributed to the effect of anomalous wind while the effect of the mean moisture change was small. The impact of the local meridional wind anomaly was critical in SC, while in AU and CA the zonal wind component dominated. The precipitation extremes had a significant increase from the early to late twentieth century. The cumulative extreme precipitation amount increased by 140%–150% in SC and AU and by 100% in CA, and the number of the extreme days increased by 110% in SC and AU and 70% in CA. Such increases are consistent with the strengthening of the high-frequency (HF; with a period less than 10 days) variability across the globe. The pattern of percentage increase of the HF variability resembles the pattern of percentage increase of MJO-scale precipitation. This implies that the MJO teleconnection pattern could exert a large-scale control on the HF variability and weather extremes. Significance Statement The Madden–Julian oscillation (MJO), a dominant mode of tropical intraseasonal oscillations, plays an important role in affecting global weather and climate. To understand to what extent the MJO and associated teleconnections change with global warming, this study adopts an alternative approach by analyzing a century-long reanalysis dataset rather than the outputs of climate models that consist of large uncertainties due to poor MJO simulations. We found that MJO-induced tropical and midlatitude precipitation and associated extremes and high-frequency variabilities were strengthened during the past century while the global mean surface temperature increases approximately by 1°C. The results derived from the present study provide a basis for assessing the future MJO behavior and associated climate impacts and for improving the extended-range prediction of severe weather and climate extremes.

Detection and attribution of climate change: A deep learning and variational approach

Abstract Twelve climate models and observations are used to attribute the global mean surface temperature (GMST) changes from 1900 to 2014 to external climate forcings. The external forcings are decomposed into the effects of the well-mixed greenhouse gas concentration variation, the effects of anthropogenic aerosol concentration changes, and the effects of natural forcings. First, a convolutional neural network (CNN) is trained to estimate the simulated historical GMST from single-forcing experiments using outputs from the multi-model ensemble. We then use this CNN to solve the attribution problem using an original variational inversion approach. The variational inversion is first validated using historical climate simulations as pseudo-observations. Then we perform an inversion from observations. This provides a distribution of the GMST resulting from the three forcings. For 2014, inversions estimate that the greenhouse gases changes are responsible for a GMST anomaly within [0.8 $ {}^{\circ } $ C,1.9 $ {}^{\circ } $ C], while anthropogenic aerosols and natural forcings anomalies are within [−0.7 $ {}^{\circ } $ C,−0.1 $ {}^{\circ } $ C] and [−0.1 $ {}^{\circ } $ C,0.3 $ {}^{\circ } $ C], respectively. The method designed here can be adapted and extended to attribute the changes of other variables or to focus on the regional scale.

A strong mitigation scenario maintains climate neutrality of northern peatlands

A strong mitigation scenario maintains climate neutrality of northern peatlands

Finite difference schemes for time-dependent convection <i>q</i>-diffusion problem

<abstract><p>The energy balance ordinary differential equations (ODEs) model of climate change is extended to the partial differential equations (PDEs) model with convections and <italic>q</italic>-diffusions. Instead of integer order second-order partial derivatives, partial <italic>q</italic>-derivatives are considered. The local stability analysis of the ODEs model is established using the Routh-Hurwitz criterion. A numerical scheme is constructed, which is explicit and second-order in time. For spatial derivatives, second-order central difference formulas are employed. The stability condition of the numerical scheme for the system of convection <italic>q</italic>-diffusion equations is found. Both types of ODEs and PDEs models are solved with the constructed scheme. A comparison of the constructed scheme with the existing first-order scheme is also made. The graphical results show that global mean surface and ocean temperatures escalate by varying the heat source parameter. Additionally, these newly established techniques demonstrate predictability.</p></abstract>

On the Solar Climate of the Moon and the Resulting Surface Temperature Distribution

The solar climate of our Moon is analyzed using the results of numerical simulations and the recently released data of the Diviner Lunar Radiometer Experiment (DLRE) to assess (a) the resulting distribution of the surface temperature, (b) the related global mean surface temperature Ts>, and (c) the effective radiation temperature Te often considered as a proxy for Ts> of rocky planets and/or their natural satellites, where Te is based on the global radiation budget of the well-known “thought model” of the Earth in the absence of its atmosphere. Because the Moon consists of similar rocky material like the Earth, it comes close to this thought model. However, the Moon’s astronomical features (e.g., obliquity, angular velocity of rotation, position relative to the disc of the solar system) differ from that of the Earth. Being tidally locked to the Earth, the Moon’s orbit around the Sun shows additional variation as compared to the Earth’s orbit. Since the astronomical parameters affect the solar climate, we predicted the Moon’s orbit coordinates both relative to the Sun and the Earth for a period of 20 lunations starting May 24, 2009, 00:00 UT1 with the planetary and lunar ephemeris DE430 of the Jet Propulsion Laboratory of the California Institute of Technology. The results revealed a mean heliocentric distance for the Moon and Earth of 1.00124279 AU and 1.00166376 AU, respectively. The mean geocentric distance of the Moon was 384792 km. The synodic and draconic months deviated from their respective means in a range of -5.7 h to 6.9 h and ±3.4 h, respectively. The deviations of the anomalistic months from their mean range between -2.83 d and 0.97 d with the largest negative deviations occurring around the points of inflection in the curve that represents the departure of the synodic month from its mean. Based on the two successive passages of the Sun through the ascending node of the lunar equator plane, the time interval between them corresponds to 347.29 days, i.e., it is slightly longer than the mean draconic year of 346.62 days. We computed the local solar insolation as input to the multilayer-force restore method of Kramm et al. (2017) that is based on the local energy budget equation. Due to the need to spin up the distribution of the regolith temperature to equilibrium, analysis of the model results covers only the last 12 lunations starting January 15, 2010, 07:11 UT1. The predicted slab temperatures, Tslab, considered as the realistic surface temperatures, follow the bolometric temperatures, Tbol, acceptably. According to all 24 DLRE datasets related to the subsolar longitude øss, the global averages of the bolometric temperature amounts to Tbol=201.1k± 0.6K. Based on the globally averaged emitted infrared radiation of FIR>=290.5W·m-2± 3.0W·m-2 derived from the 24 DLRE datasets, the effective radiative temperature of the Moon is Te, M>=Tbol>1/4=271.0k± 0.7K so that Tbol>≅0.742Te, M. The DLRE observations suggest that in the case of rocky planets and their natural satellites, the globally averaged surface temperature is notably lower than the effective radiation temperature. They differ by a factor that depends on the astronomical parameters especially on the angular velocity of rotation.

Evaluation of climate variability and change in ACCESS historical simulations for CMIP6

We analyse and document the historical simulations performed by two versions of the Australian Community Climate and Earth System Simulator (ACCESS-CM2 and ACCESS-ESM1.5) for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Three ensemble members from each model are used to compare the simulated seasonal-mean climate, climate variability and climate change with observations over the historical period. Where appropriate, we also compare the ACCESS model results with the results from 36 other CMIP6 models. We find that the simulations of the winter and summer mean climates (over the global domain) by the two ACCESS models are similar to or better than most of the other CMIP6 models for surface temperature, precipitation and surface specific humidity. For sea-level pressure, both ACCESS models perform worse than most other models. The spatial structures of the prominent climate variability modes (ENSO, IOD, IPO and AMO) also compare favourably with the corresponding observed structures. However, the results for the simulation of the models’ temporal variability are mixed. In particular, whereas ACCESS-ESM1.5 simulates ENSO events with ~3-year periods (that are closer to the observed periods of 3–7 years), the ACCESS-CM2 simulates ENSO events having quasi-biennial periods. However, ACCESS-CM2 has a much smaller bias (−0.1 W m−2) in present-day top-of-the-atmosphere energy balance than ACCESS-ESM1.5 (−0.6 W m−2). The ACCESS models simulate the anthropogenic climate change signal in historical global-mean surface temperature reasonably well, although the simulated signal variances are ~10% weaker than the observed signal variance (a common bias in most CMIP6 models). Both models also well simulate the major features of observed surface temperature changes, as isolated using a multiple regression model. Despite some identified biases, the two ACCESS models provide high-quality climate simulations that may be used in further analyses of climate variability and change.

Investigating climate tipping points under various emission reduction and carbon capture scenarios with a stochastic climate model

We study the mitigation of climate tipping point transitions using an energy balance model. The evolution of the global mean surface temperature is coupled with the CO 2 concentration through the green-house effect. We model the CO 2 concentration with a stochastic delay differential equation (SDDE), accounting for various carbon emission and capture scenarios. The resulting coupled system of SDDEs exhibits a tipping point phenomena: if CO 2 concentration exceeds a critical threshold (around 478 ppm ), the temperature experiences an abrupt increase of about six degrees Celsius. We show that the CO 2 concentration exhibits a transient growth which may cause a climate tipping point, even if the concentration decays asymptotically. We derive a rigorous upper bound for the CO 2 evolution which quantifies its transient and asymptotic growths, and provides sufficient conditions for evading the climate tipping point. Combining this upper bound with Monte Carlo simulations of the stochastic climate model, we investigate the emission reduction and carbon capture scenarios that would avert the tipping point.

Mathematical Modelling of Climate Change and Variability in the Context of Outdoor Ergonomics

The current climate change, unlike previous ones, is caused by human activity and is characterized by an unprecedented rate of increase in the near-surface temperature and an increase in the frequency and intensity of hazardous weather and climate events. To survive, society must be prepared to implement adaptation strategies and measures to mitigate the negative effects of climate change. This requires, first of all, knowledge of how the climate will change in the future. To date, mathematical modelling remains the only method and effective tool that is used to predict the climate system’s evolution under the influence of natural and anthropogenic perturbations. It is important that mathematics and its methods and approaches have played a vital role in climate research for several decades. In this study, we examined some mathematical methods and approaches, primarily, mathematical modelling and sensitivity analysis, for studying the Earth’s climate system, taking into account the dependence of human health on environmental conditions. The essential features of stochastic climate models and their application for the exploration of climate variability are examined in detail. As an illustrative example, we looked at the application of a low-order energy balance model to study climate variability. The effects of variations in feedbacks and the climate system’s inertia on the power spectrum of global mean surface temperature fluctuations that characterized the distribution of temperature variance over frequencies were estimated using a sensitivity analysis approach. Our confidence in the obtained results was based on the satisfactory agreement between the theoretical power spectrum that was derived from the energy balance model and the power spectrum that was obtained from observations and coupled climate models, including historical runs of the CMIP5 models.

Slow Modes of Global Temperature Variability and Their Impact on Climate Sensitivity Estimates

AbstractInternal climate variability confounds estimates of the climate response to forcing but offers an opportunity to examine the dynamics controlling Earth’s energy budget. This study analyzes the time-evolving impact of modes of low-frequency internal variability on global-mean surface temperature (GMST) and top-of-atmosphere (TOA) radiation in preindustrial control simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The results show that the slow modes of variability with the largest impact on decadal GMST anomalies are focused in high-latitude ocean regions, where they have a minimal impact on global TOA radiation. When these regions warm, positive shortwave cloud and sea ice–albedo feedbacks largely cancel the negative feedback of outgoing longwave radiation, resulting in a weak net radiative feedback. As a consequence of the weak net radiative feedback, less energy is required to sustain these long-lived temperature anomalies. In contrast to these weakly radiating high-latitude modes, El Niño–Southern Oscillation (ENSO) has a large impact on the global energy budget, such that it remains the dominant influence on global TOA radiation out to decadal and longer time scales, despite its primarily interannual time scale. These results show that on decadal and longer time scales, different processes control internal variability in GMST than control internal variability in global TOA radiation. The results are used to quantify the impact of low-frequency internal variability and ENSO on estimates of climate sensitivity from historical GMST and TOA-radiative-imbalance anomalies.

Simulation of the mid-Pliocene Warm Period using HadGEM3: experimental design and results from model–model and model–data comparison

Abstract. Here we present the experimental design and results from a new mid-Pliocene simulation using the latest version of the UK's physical climate model, HadGEM3-GC31-LL, conducted under the auspices of CMIP6/PMIP4/PlioMIP2. Although two other palaeoclimate simulations have been recently run using this model, they both focused on more recent periods within the Quaternary, and therefore this is the first time this version of the UK model has been run this far back in time. The mid-Pliocene Warm Period, ∼3 Ma, is of particular interest because it represents a time period when the Earth was in equilibrium with CO2 concentrations roughly equivalent to those of today, providing a possible analogue for current and future climate change. The implementation of the Pliocene boundary conditions is firstly described in detail, based on the PRISM4 dataset, including CO2, ozone, orography, ice mask, lakes, vegetation fractions and vegetation functional types. These were incrementally added into the model, to change from a pre-industrial setup to a Pliocene setup. The results of the simulation are then presented, which are firstly compared with the model's pre-industrial simulation, secondly with previous versions of the same model and with available proxy data, and thirdly with all other models included in PlioMIP2. Firstly, the comparison with the pre-industrial simulation suggests that the Pliocene simulation is consistent with current understanding and existing work, showing warmer and wetter conditions, and with the greatest warming occurring over high-latitude and polar regions. The global mean surface air temperature anomaly at the end of the Pliocene simulation is 5.1 ∘C, which is the second highest of all models included in PlioMIP2 and is consistent with the fact that HadGEM3-GC31-LL has one of the highest Effective Climate Sensitivities of all CMIP6 models. Secondly, the comparison with previous generation models and with proxy data suggests a clear increase in global sea surface temperatures as the model has undergone development. Up to a certain level of warming, this results in a better agreement with available proxy data, and the “sweet spot” appears to be the previous CMIP5 generation of the model, HadGEM2-AO. The most recent simulation presented here, however, appears to show poorer agreement with the proxy data compared with HadGEM2 and may be overly sensitive to the Pliocene boundary conditions, resulting in a climate that is too warm. Thirdly, the comparison with other models from PlioMIP2 further supports this conclusion, with HadGEM3-GC31-LL being one of the warmest and wettest models in all of PlioMIP2, and if all the models are ordered according to agreement with proxy data, HadGEM3-GC31-LL ranks approximately halfway among them. A caveat to these results is the relatively short run length of the simulation, meaning the model is not in full equilibrium. Given the computational cost of the model it was not possible to run it for a longer period; a Gregory plot analysis indicates that had it been allowed to come to full equilibrium, the final global mean surface temperature could have been approximately 1.5 ∘C higher.

Estimating a social cost of carbon for global energy consumption.

Estimates of global economic damage caused by carbon dioxide (CO2) emissions can inform climate policy1-3. The social cost of carbon (SCC) quantifies these damages by characterizing how additional CO2 emissions today impact future economic outcomes through altering the climate4-6. Previous estimates have suggested that large, warming-driven increases in energy expenditures could dominate the SCC7,8, but they rely on models9-11 that are spatially coarse and not tightly linked to data2,3,6,7,12,13. Here we show that the release of one ton of CO2 today is projected to reduce total future energy expenditures, with most estimates valued between -US$3 and -US$1, depending on discount rates. Our results are based on an architecture that integrates global data, econometrics and climate science to estimate local damages worldwide. Notably, we project that emerging economies in the tropics will dramatically increase electricity consumption owing to warming, which requires critical infrastructure planning. However, heating reductions in colder countries offset this increase globally. We estimate that 2099 annualglobal electricity consumption increases by about 4.5 exajoules (7 per cent of current global consumption)per one-degree-Celsius increase in global mean surfacetemperature (GMST), whereas direct consumption of other fuels declines by about 11.3 exajoules (7 per cent of current global consumption) per one-degree-Celsiusincrease in GMST. Our finding of net savings contradicts previous research7,8, because global data indicate that many populations will remain too poor for most of the twenty-first century to substantially increase energy consumption in response to warming. Importantly, damage estimates would differ if poorer populations were given greater weight14.

Global Wave Height Slowdown Trend during a Recent Global Warming Slowdown

It has been reported that global warming results in the increase of globally averaged wave heights. What happened to the global-averaged wave heights during the global warming slowdown period (1999–2013)? Using reanalysis products, together with remote sensing and in situ observational data, it was found that the temporal variation pattern of the globally averaged wave heights was similar to the slowdown trend in the increase in global mean surface temperature during the same period. The analysis of the spatial distribution of trends in wave height variation revealed different rates in global oceans: a downward trend in the northeastern Pacific and southern Indian Ocean, and an upward trend in other regions. The decomposition of waves into swells and wind waves demonstrates that swells dominate global wave height variations, which indicates that local sea surface winds indirectly affect the slowdown in the rate of wave height growth.

Uniting the Global Gastroenterology Community to Meet the Challenge of Climate Change and Non-Recyclable Waste

Uniting the Global Gastroenterology Community to Meet the Challenge of Climate Change and Non-Recyclable Waste

Conversion of the Knutson et al. (2020) Tropical Cyclone Climate Change Projections to Risk Model Baselines

AbstractKnutson et al. (2020) recently published a meta-study that gives multi-model projections for changes in various properties of tropical cyclones under climate change. They considered frequency of tropical cyclones, frequency of very intense tropical cyclones, intensity of tropical cyclones, and total rainfall rate of tropical cyclones. For each of these properties, they reported changes globally and by basin for the six major tropical cylone basins. The changes were presented as the change that would occur with 2 °C warming of global mean surface temperature. These projections are potentially of great use to the tropical cyclone risk modeling community. However, most risk models use temporal baselines, such as the period from 1950 to 2019, and the Knutson et al. results can only be applied to risk models after some steps of adjustment involving past and future global mean surface temperature values. We derive the necessary adjustments and present and discuss some of the resulting projections, for different properties, basins, RCPs and baselines. We find that the results are sensitive to the baseline being used, which implies that users of tropical cyclone risk models need to make sure they clearly understand what baseline their model represents before they adjust the model for climate change. One part of our analysis derives estimates of the implied impact of climate change so far on TC properties, relative to a representative baseline. The computer code we use to calculate the adjustments is available online.

Similar patterns of tropical precipitation and circulation changes under solar and greenhouse gas forcing

Theory and model evidence indicate a higher global hydrological sensitivity for the same amount of surface warming to solar as to greenhouse gas (GHG) forcing, but regional patterns are highly uncertain due to their dependence on circulation and dynamics. We analyse a multi-model ensemble of idealized experiments and a set of simulations of the last millennium and we demonstrate similar global signatures and patterns of forced response in the tropical Pacific, of higher sensitivity for the solar forcing. In the idealized simulations, both solar and GHG forcing warm the equatorial Pacific, enhance precipitation in the central Pacific, and weaken and shift the Walker circulation eastward. Centennial variations in the solar forcing over the last millennium cause similar patterns of enhanced equatorial precipitation and slowdown of the Walker circulation in response to periods with stronger solar forcing. Similar forced patterns albeit of considerably weaker magnitude are identified for variations in GHG concentrations over the 20th century, with the lower sensitivity explained by fast atmospheric adjustments. These findings differ from previous studies that have typically suggested divergent responses in tropical precipitation and circulation between the solar and GHG forcings. We conclude that tropical Walker circulation and precipitation might be more susceptible to solar variability rather than GHG variations during the last-millennium, assuming comparable global mean surface temperature changes.

Double benefit of limiting global warming for tropical cyclone exposure

Tropical cyclone (TC) impacts are expected to worsen under continued global warming and socio-economic development. Here we combine TC simulations with an impact model to quantify country-level population exposure to TC winds for different magnitudes of global mean surface temperature increase and future population distributions. We estimate an annual global TC exposure increase of 26% (33 million people) for a 1 °C increase in global mean surface temperature, assuming present-day population. The timing of warming matters when additionally accounting for population change, with global population projected to peak around mid-century and decline thereafter. A middle-of-the-road socio-economic scenario combined with 2 °C of warming around 2050 increases exposure by 41% (52 million). A stronger mitigation scenario reaching 2 °C around 2100 limits this increase to 20% (25 million). Rapid climate action therefore avoids interference with peak global population timing and limits climate-change-driven exposure. Cumulatively, over 1.8 billion people could be saved by 2100.

Suppressed Late‐20th Century Warming in CMIP6 Models Explained by Forcing and Feedbacks

AbstractFor the 1960–2000 period, the latest generation of climate models (Coupled Model Intercomparison Project Phase 6 [CMIP6]) shows less global mean surface temperature change relative to pre‐industrial than that seen in observations. In contrast, the previous generation of models (CMIP5) performed well over this period. It has been hypothesized that this suppressed late‐20th Century warming seen in CMIP6 is caused by a stronger aerosol forcing. However, we find this to be only part of the story. Not only is the aerosol forcing marginally more negative in CMIP6 compared to CMIP5, the greenhouse gas forcing in CMIP6 is also weaker than in CMIP5. These forcing differences are amplified by differences in climate sensitivity between the CMIP5 and CMIP6 ensemble, which leads to both a stronger aerosol cooling over 1960–1990 and a stronger greenhouse gas induced warming from 1990, returning the warming post‐2000 toward the observed level.

Methane removal and the proportional reductions in surface temperature and ozone.

Mitigating climate change requires a diverse portfolio of technologies and approaches, including negative emissions or removal of greenhouse gases. Previous literature focuses primarily on carbon dioxide removal, but methane removal may be an important complement to future efforts. Methane removal has at least two key benefits: reducing temperature more rapidly than carbon dioxide removal and improving air quality by reducing surface ozone concentration. While some removal technologies are being developed, modelling of their impacts is limited. Here, we conduct the first simulations using a methane emissions-driven Earth System Model to quantify the climate and air quality co-benefits of methane removal, including different rates and timings of removal. We define a novel metric, the effective cumulative removal, and use it to show that each effective petagram of methane removed causes a mean global surface temperature reduction of 0.21 ± 0.04°C and a mean global surface ozone reduction of 1.0 ± 0.2 parts per billion. Our results demonstrate the effectiveness of methane removal in delaying warming thresholds and reducing peak temperatures, and also allow for direct comparisons between the impacts of methane and carbon dioxide removal that could guide future research and climate policy.This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

Simulated response of soil organic carbon density to climate change in the Northern Tibet permafrost region

Climate warming can enhance the decomposition of soil organic matter (SOM), thereby increasing the rate of carbon release from soils. In permafrost regions, climate warming alters the nature of freeze–thaw cycles by affecting sub-surface hydrology and soil temperature, thereby impacting the decomposition of SOM. However, this process is rarely considered in projections of the long-term dynamics of soil organic carbon (SOC) on the Tibetan Plateau (TP) in response to climate change. Here, we employ the CENTURY-FTC model, which implements a freeze–thaw module in the CENTURY model, to simulate the response of top soil organic carbon density (SOCD) at 0–20 cm depth to climate change in the permafrost region of Northern Tibet. Our findings suggest that (i) the average SOCD was 2.123 kg C m−2 in 2015; (ii) the SOCD decreased at an average rate of 0.7 × 10−3 kg C m−2 year−1 for the period 1961–2015; and (iii) SOCD decreases spatially from south to north across Northern Tibet. Under various climate scenarios, the SOCD is projected to decrease significantly throughout Northern Tibet from 2016 to 2050, with the decline being most pronounced under the representative concentration-pathway (RCP) 8.5 scenario, which projects an increase in global mean surface temperature of 4.5 °C by 2100. Superimposed on this pattern, regional differences might be driven by vegetation type, with the largest decrements occurring in southern, alpine-meadow-dominated areas and the smallest in the northern alpine desert. We propose that this declining trend will be enhanced as climate warming continues and might be amplified by the increase in freeze–thaw processes. The addition of a freeze–thaw module to the CENTURY model changes projections of SOCD change due to climate. Whereas the freeze–thaw cycle has had little impact during the baseline period, its influence is likely to increase as climate warming continues. By 2050, freeze–thaw processes are projected to contribute 3% to SOCD decline under the RCP2.6 scenario, which projects a rise in global mean surface temperature of 1.5 °C by 2100, and as much as 10% under the RCP8.5 scenario. In general, future warming is likely to result in declining SOCD throughout Northern Tibet and a reduction in the capacity of alpine soils to sequester carbon.