Global Mean Surface Temperature Research Articles

Foundations of attribution in climate-change science

Attribution—the explanation of an observed change in terms of multiple causal factors—is the cornerstone of climate-change science. For anthropogenic climate change (ACC), the central causal factor is evidently ACC itself, and one of the primary tools used to reveal ACC is aggregation, or grouping together, of data, e.g. global mean surface temperature. Whilst this approach has served climate-change science well, the landscape is changing rapidly. First, there is an increasing focus on regional or local aspects of climate change, and on singular or unprecedented events, which require varying degrees of disaggregation. Relatedly, climate change is increasingly apparent in observations at the local scale, which is challenging the primacy of climate model simulations. Finally, the explosion of climate data is leading to more phenomena-laden methodologies such as machine learning. All this demands a re-think of how attribution is performed and causal explanations are constructed. Here we use Lloyd’s ‘Logic of Research Questions’ framework to show how the way in which the attribution question is framed can strongly constrain its possible and responsive answers. To address the Research Question ‘What was the effect of ACC on X?’ (RQ1), scientists generally consider the question ‘What were the causal factors leading to X, and was ACC among them?’. If the causal factors include only external forcing and internal variability (RQ2), then answering RQ2 also answers RQ1. However, this unconditional attribution is not always possible. In such cases, allowing the causal factors to include elements of the climate system itself (RQ3)—the conditional, storyline approach—is shown to allow for a wider range of possible and responsive answers than RQ2, including that of singular causation. This flexibility is important when uncertainties are high. As a result, the conditional RQ3 mitigates against the sort of epistemic injustice that can arise from the unconditional RQ2.

Unexpected cooling Eurasia during February of global-warming slowdown: Roles of North Atlantic and Arctic Oceans

The latest global mean surface temperature (GMST) indices from several datasets all show a significant warming trend during the so-called period of global-warming slowdown (i.e., 1998–2012), albeit at a more modest rise compared with the satellite era in the 20th century. Via the Mann-Kendall trend test, this study further examined the trend of GMST from a monthly perspective. It is surprising to find that a significant negative trend occurs only in February and the robust cooling is most evident in the mid–high latitudes of Eurasia. Then an empirical orthogonal function (EOF) based on multi-sources data (MS-EOF) was applied on the February surface air temperature (SAT) over Eurasia. Focus is given on the characteristics and factors of the leading MS-EOF mode (MS-EOF1), as in the trend pattern, which features a uniform cooling mode dominating the mid–high latitudes of Eurasian continent. The key circulation features a large-scale eastward-shift North Atlantic Oscillation (NAO)-like pattern, which is, in a negative phase, favourable for weakening the polar-front jet stream and transporting cold air from the Arctic regions to the mid-high latitudes of Eurasian continent. Relevant precursor signals can be traced back to the anomalous North Atlantic sea surface temperature (SST) and Barents–Kara sea ice in the preceding autumn, together contributing significantly to the cooling February over middle- to high-latitude Eurasia and is verified by a linear baroclinic model. This finding emphasizes the important contribution of cooling February to the famous global-warming slowdown.

More Accurate Climate Trend Attribution by Using Cointegrating Vector Time Series Models

Adapting to human-induced climate change is becoming an increasingly important aspect of sustainable development. To be able to do this effectively, it is important to know how much human influence has contributed to observed climate trends. Climate detection and attribution (D&A) studies achieve this by estimating scaling factors usually obtained by performing a least squares regression of the observed trending climate variable on the equivalent variable simulated by a climate model. This study proposed instead to estimate scaling factors by using the econometric approach of dynamically modelling the time series as a cointegrating Vector Auto-Regressive (VAR) time series process. It is shown that a 2nd-order cointegrating VAR(2) model is theoretically justified if the observed and simulated variables can be represented as a one-box AR(1) response to a common integrated forcing. The VAR(2) model can be expressed as a Vector Error-Correction Model (VECM) and then fitted to the data to obtain the cointegration relationship, the stationary linear combination of the two variables, from which the scaling factor is then easily obtained. Estimates of the scaling factor from the VAR(2) model are critically compared to those from Ordinary Least Squares (OLS) and Total Least Squares (TLS) for annual Global Mean Surface Temperature (GMST) data simulated by a simple stochastic model of the carbon–climate system and for historical simulations from 16 climate models in the Coupled Model Intercomparison Project 5 (CMIP5) experiment. Results from the toy model simulations show that the slope estimates from OLS are negatively biased, TLS estimates are less biased but have high variance, and the VAR(2) estimates are unbiased and have lower variance and provide the most accurate estimates with smallest mean squared error. Similar behaviour is noted in the CMIP5 data. Hypothesis tests on the VAR(2) fits found strong evidence of a cointegrating relationship with the observations for all the CMIP5 simulations.

Critical Review on Radiative Forcing and Climate Models for Global Climate Change since 1970

This review identifies a critical problem in the fundamental physics of current climate models. The large greenhouse effect of rising CO2 assumed in climate models is assessed by six key observations from ground- and satellite-based measurements. This assessment is enhanced by statistical analyses and model calculations of global or regional mean surface temperature changes by conventional climate models and by a conceptual quantum physical model of global warming due to halogen-containing greenhouse gases (halo-GHGs). The postulated large radiative forcing of CO2 in conventional climate models does not agree with satellite observations. Satellite-observed warming pattern resembles closely the atmospheric distribution of chlorofluorocarbons (CFCs). This review helps understand recent remarkable observations of reversals from cooling to warming in the lower stratosphere over most continents and in the upper stratosphere at high latitudes, surface warming cessations in the Antarctic, North America, UK, and Northern-Hemisphere (NH) extratropics, and the stabilization in NH or North America snow cover, since the turn of the century. The complementary observation of surface temperature changes in 3 representative regions (Central England, the Antarctic, and the Arctic) sheds new light on the primary mechanism of global warming. These observations agree well with not CO2-based climate models but the CFC-warming quantum physical model. The latter offers parameter-free analytical calculations of surface temperature changes, exhibiting remarkable agreement with observations. These observations overwhelmingly support an emerging picture that halo-GHGs made the dominant contribution to global warming in the late 20th century and that a gradual reversal in warming has occurred since ~2005 due to the phasing out of halo-GHGs. Advances and insights from this review may help humans make rational policies to reverse the past warming and maintain a healthy economy and ecosystem.

Climate Resilience and Ecological Dynamics of the Mpameso Forest Reserve in Ghana: Insights from Land Surface Temperature Analysis

Climate change, characterized by shifts in weather parameters and escalating climate-related risks, has emerged as a global environmental concern. Among these concerns, chronic heat waves have garnered attention due to their far-reaching implications. This study delves into the profound impact of chronic heat waves on the Mpameso forest reserve in Ghana, an ecosystem of vital importance. Chronic heat waves have induced notable changes in the local climate, marked by increased temperatures, elevated evapotranspiration rates, and alterations in rainfall patterns over the past three decades. Ghana, like many regions worldwide, has experienced shifts in seasons and a rising global mean surface temperature, contributing to the intensification of global warming. The repercussions of chronic heat waves are particularly significant within forest reserves, and their effects on Ghana's Dormaa Central Municipality are explored in detail. Vegetation cover in Ghana has faced recurrent challenges from extreme heat waves, leading to diminished forested areas, heightened evapotranspiration rates, and instances of drought-induced water scarcity. To assess the severity of these impacts and devise proactive environmental management strategies, this study leverages the power of satellite remote sensing. Utilizing Landsat imagery, the study examines changes in Land Surface Temperature (LST) alongside the Normalized Difference Vegetation Index (NDVI) and Evapotranspiration (ET) within the Dormaa municipal area. Pearson correlation analysis is employed to uncover the intricate relationship between LST, vegetation health, and ET within the Mpameso forest reserve. The study's compelling findings reveal that chronic heat waves predominantly affect the settlement areas surrounding the forest reserve, sparing the reserve itself from significant LST-related health impacts. This nuanced understanding underscores the importance of proactive conservation efforts and climate resilience planning, emphasizing the value of integrated approaches to safeguarding vital ecosystems in the face of mounting climate extremes.

Radiative Forcing From the 2014–2022 Volcanic and Wildfire Injections

AbstractVolcanic and wildfire events between 2014 and 2022 injected ∼3.2 Tg of sulfur dioxide and 0.8 Tg of smoke aerosols into the stratosphere. With injections at higher altitudes and lower latitudes, the simulated stratospheric lifetime of the 2014–2022 injections is about 50% longer than the volcanic 2005–2013 injections. The simulated global mean effective radiative forcing (ERF) of 2014–2022 is −0.18 W m−2, ∼40% of the ERF of the period of 1991–1999 with a large‐magnitude volcanic eruption (Pinatubo). Our climate model suggests that the stratospheric smoke aerosols generate ∼60% more negative ERF than volcanic sulfate per unit aerosol optical depth. Studies that fail to account for the different radiative properties of wildfire smoke relative to volcanic sulfate will likely underestimate the negative stratospheric forcings. Our analysis suggests that stratospheric injections offset 20% of the increase in global mean surface temperature between 2014–2022 and 1999–2002.

Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 ^{circ }C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region.

Vulnerability assessment for climate adaptation planning in a Mediterranean basin

ABSTRACT The Iberian Peninsula is a climate change hotspot, where the temperature is increasing faster than the global annual mean surface temperature, with the largest reduction of precipitation. Consequently, freshwater availability is expected to decrease substantially. In this context, freshwater systems are especially vulnerable in terms of meeting the water demands and ecosystem requirements we know today. In this paper, we present an extension of the eco-engineering decision scaling (EEDS) method to explore trade-offs in agricultural and ecologic metrics at the catchment scale across a range of unknown future hydrological and climate states. The extended EEDS method evaluates current water resource management rules focusing on agricultural and ecologic objectives, identifies climate hazards that make the system fail and assesses climate risk in three time horizons for the design of adaptation measures. The case study is the Serpis River basin, Spain, where 72% of available water is used for agricultural purposes.

Effects of ozone levels on climate through Earth history

Abstract. Molecular oxygen in our atmosphere has increased from less than a part per million in the Archean Eon to a fraction of a percent in the Proterozoic and finally to modern levels during the Phanerozoic. The ozone layer formed with the early Proterozoic oxygenation. While oxygen itself has only minor radiative and climatic effects, the accompanying ozone has important consequences for Earth climate. Using the Community Earth System Model (CESM), a 3-D general circulation model (GCM), we test the effects of various levels of ozone on Earth's climate. When CO2 is held constant, the global-mean surface temperature decreases with decreasing ozone, with a maximum drop of ∼3.5 K at near total ozone removal. By supplementing our GCM results with 1-D radiative flux calculations, we are able to test which changes to the atmosphere are responsible for this temperature change. We find that the surface temperature change is caused mostly by the stratosphere being much colder when ozone is absent; this makes it drier, substantially weakening the greenhouse effect. We also examine the effect of the structure of the upper troposphere and lower stratosphere on the formation of clouds and on the global circulation. At low ozone, both high and low clouds become more abundant due to changes in the tropospheric stability. These generate opposing shortwave and longwave radiative forcings that are nearly equal. The Hadley circulation and tropospheric jet streams are strengthened, while the stratospheric polar jets are weakened, the latter being a direct consequence of the change in stratospheric temperatures. This work identifies the major climatic impacts of ozone, an important piece of the evolution of Earth's atmosphere.

A comparison of the climate and carbon cycle effects of carbon removal by afforestation and an equivalent reduction in fossil fuel emissions

Abstract. Afforestation and reduction of fossil fuel emissions are two major components of climate mitigation policies. However, their effects on the Earth's climate are different because a reduction of fossil fuel emissions directly alters the biogeochemical cycle of the climate system and modifies the physics of the atmosphere via its impact on radiation and the energy budget, while afforestation causes biophysical changes in addition to changes in the biogeochemical cycle. In this paper, we compare the climate and carbon cycle consequences of carbon removal by afforestation and an equivalent fossil fuel emission reduction using simulations from an intermediate complexity Earth system model. We performed two major sets of idealized simulations in which fossil fuel emissions follow extended Shared Socioeconomic Pathway (SSP) scenarios (SSP2-4.5, 3-7.0, and 5-8.5), and equal amounts of carbon are removed by afforestation in one set and by a reduction in fossil fuel emissions in another set. Our simulations show that the climate is cooler by 0.36, 0.47, and 0.42 ∘C in the long term (2471–2500) in the case of reduced fossil fuel emissions compared to the case with afforestation when the emissions follow the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. The global mean surface temperature is cooler in the reduced fossil fuel emissions case compared to the afforestation case because the net biophysical effect of warming from afforestation partly offsets the biogeochemical cooling effect of afforestation. Thus, in terms of climate benefits, reducing fossil fuel emissions could be relatively more beneficial than afforestation for the same amount of carbon removed from the atmosphere. However, a robust understanding of the processes that govern the biophysical effects of afforestation should be improved before considering our results for climate policy.

Climate Change: Linear and Nonlinear Causality Analysis

The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional physics-based models. Monthly data on potential climate change causal factors, including greenhouse gas concentrations, sunspot numbers, humidity, ice sheets mass, and sea ice coverage, from January 2003 to December 2021, have been utilized in the analysis. We first applied the vector autoregressive model (VAR) and Granger causality test to gauge the linear Granger causal relationships among climate factors. We then adopted the vector error correction model (VECM) as well as the autoregressive distributed lag model (ARDL) to quantify the linear long-run equilibrium and the linear short-term dynamics. Cointegration analysis has also been adopted to examine the dual directional Granger causalities. Furthermore, in this work, we have presented a novel pipeline based on the artificial neural network (ANN) and the VAR and ARDL models to detect nonlinear causal relationships embedded in the data. The results in this study indicate that the global sea level rise is affected by changes in ice sheet mass (both linearly and nonlinearly), global mean temperature (nonlinearly), and the extent of sea ice coverage (nonlinearly and weakly); whereas the global mean temperature is affected by the global surface mean specific humidity (both linearly and nonlinearly), greenhouse gas concentration as measured by the global warming potential (both linearly and nonlinearly) and the sunspot number (only nonlinearly and weakly). Furthermore, the nonlinear neural network models tend to fit the data closer than the linear models as expected due to the increased parameter dimension of the neural network models. Given that the information criteria are not generally applicable to the comparison of neural network models and statistical time series models, our next step is to examine the robustness and compare the forecast accuracy of these two models using the soon-available 2022 monthly data.

Tropical Cyclones and Climate Change: Global Landfall Frequency Projections Derived from Knutson et al.

Abstract Projections of changes in tropical cyclone (TC) characteristics under climate change are of great interest to those affected by TCs. In a recent paper, Knutson et al. combined a large number of previous results to produce projections consisting of distributions of possible future TC frequencies, intensities, and rainfall rates. These distributions provide a great resource for users of TC information. However, to apply the distributions to impacts models may require the user to solve a number of technical challenges including modeling correlations, fitting distributions, interpolation, and converting the projections to properties at landfall. I consider the frequency and intensity changes, and implement solutions for each of these challenges using a combination of recently published research and a new methodology. This allows me to produce a dataset of TC projections that give frequency changes as a function of landfall region and intensity. Mean intensity changes can then be derived from frequency changes. The projections are presented in a format that allows them to be used in impacts models without further processing. The interpolation and landfall adjustments tend to increase the frequency changes. As a result, my projections give increasing mean frequencies of hurricane-strength landfalling TCs in four out of six global basins, with mean frequencies increasing by up to 16% for a 2°C increase in global mean surface temperature. My projections are highly uncertain, but include uncertainty estimates. They are designed to be a useful resource for anyone interested in possible future TC frequencies and intensities.

Heat stored in the Earth system 1960–2020: where does the energy go?

Abstract. The Earth climate system is out of energy balance, and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI) and allows for quantifying how much heat has accumulated in the Earth system, as well as where the heat is stored. Here we show that the Earth system has continued to accumulate heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a heating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority, about 89 %, of this heat is stored in the ocean, followed by about 6 % on land, 1 % in the atmosphere, and about 4 % available for melting the cryosphere. Over the most recent period (2006–2020), the EEI amounts to 0.76±0.2 W m−2. The Earth energy imbalance is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. Moreover, this indicator is highly complementary to other established ones like global mean surface temperature as it represents a robust measure of the rate of climate change and its future commitment. We call for an implementation of the Earth energy imbalance into the Paris Agreement's Global Stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations and we also call for urgently needed actions for enabling continuity, archiving, rescuing, and calibrating efforts to assure improved and long-term monitoring capacity of the global climate observing system. The data for the Earth heat inventory are publicly available, and more details are provided in Table 4.

A service to help insurers understand the financial impacts of changing flood risk in Europe, based on PESETA IV

The European Union PESETA IV project has produced a comprehensive dataset that quantifies the impact of climate change on river flooding and damage in Europe. This data potentially has significant application in the insurance industry, but requires some steps of post-processing, and appropriate delivery mechanisms, before it can be used in practice. The post-processing approach we apply involves statistical modelling of the simulated changes in damage to estimate rates of change of log damage versus global mean surface temperature (GMST). Under linear assumptions, these rates of change can be used to calculate the change in damage between any two time-periods for any GMST scenario. Under further assumptions, these changes can be used to adjust many of the metrics of flood risk used in the insurance industry, such as average annual losses, exceedance probabilities and year loss tables. We deliver these results in two ways to suit different users in the insurance industry. First, the rates of change, and related correlations, are provided as freely available datasets for insurers who prefer to process and apply the data themselves. Second, commercial online software is provided that performs some steps of the processing and application. Many sources of uncertainty affect our results. The results must not therefore be interpreted too literally, but hopefully give a good indication of the sign and rough magnitude of the likely changes in river flood damage due to climate change. Future studies will hopefully reduce the uncertainty.

Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations

Stratospheric aerosol injection is a proposed form of solar climate invention (SCI) that could potentially reduce the amount of future warming from externally-forced climate change. However, more research is needed, as there are significant uncertainties surrounding the possible impacts of SCI, including unforeseen effects on regional climate patterns. In this study, we consider a climate model simulation of the deployment of stratospheric aerosols to maintain the global mean surface temperature at 1.5 ∘C above pre-industrial levels (ARISE-SAI-1.5). Leveraging two different machine learning methods, we evaluate when the effects of SCI would be detectable at regional scales. Specifically, we train a logistic regression model to classify whether an annual mean map of near-surface temperature or total precipitation is from future climate change under the influence of SCI or not. We then design an artificial neural network to predict how many years it has been since the deployment of SCI by inputting the regional maps from the climate intervention scenario. In both detection methods, we use feature attribution methods to spatially understand the forced climate patterns that are important for the machine learning model predictions. The differences in regional temperature signals are detectable in under a decade for most regions in the SCI scenario compared to greenhouse gas warming. However, the influence of SCI on regional precipitation patterns is more difficult to distinguish due to the presence of internal climate variability.

Assessment of Large-Scale Indices of Surface Temperature during the Historical Period in the CMIP6 Ensemble

Abstract We develop a statistical method to assess CMIP6 simulations of large-scale surface temperature change during the historical period (1850–2014), considering all time scales, allowing for the different unforced variability of each model and the observations, observational uncertainty, and variable ensemble size. The generality of this method, and the fact that it incorporates information about the unforced variability, makes it a useful model assessment tool. We apply this method to the historical simulations of the CMIP6 multimodel ensemble. We use three indices that measure different aspects of large-scale surface air temperature change: global mean, hemispheric gradient, and a recently developed index that captures the sea surface temperature (SST) pattern in the tropics (SST#; see Fueglistaler and Silvers). We use the following observations: HadCRUT5 for the first two indices, and AMIPII and ERSSTv5 for SST#. In each case, we test the hypothesis that the model’s forced response is compatible with the observations, accounting for unforced variability in both models and observations as well as measurement uncertainty. This hypothesis is accepted more often (75% of the models) for the hemispheric gradient than for the global mean, for which half of the models fail the test. The tropical SST pattern is poorly simulated in all models. Given that the tropical SST pattern can strongly modulate the relationship between energy imbalance and global-mean surface temperature anomalies on annual to decadal time scales (short-term feedback parameter), we suggest this should be a focus area for future improvements due to its potential implications for the global-mean temperature evolution in decadal time scales.

National contributions to climate change due to historical emissions of carbon dioxide, methane, and nitrous oxide since 1850

Anthropogenic emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) have made significant contributions to global warming since the pre-industrial period and are therefore targeted in international climate policy. There is substantial interest in tracking and apportioning national contributions to climate change and informing equitable commitments to decarbonisation. Here, we introduce a new dataset of national contributions to global warming caused by historical emissions of carbon dioxide, methane, and nitrous oxide during the years 1851–2021, which are consistent with the latest findings of the IPCC. We calculate the global mean surface temperature response to historical emissions of the three gases, including recent refinements which account for the short atmospheric lifetime of CH4. We report national contributions to global warming resulting from emissions of each gas, including a disaggregation to fossil and land use sectors. This dataset will be updated annually as national emissions datasets are updated.

UKESM1.1: development and evaluation of an updated configuration of the UK Earth System Model

Abstract. Many Coupled Model Intercomparison Project phase 6 (CMIP6) models have exhibited a substantial cold bias in the global mean surface temperature (GMST) in the latter part of the 20th century. An overly strong negative aerosol forcing has been suggested as a leading contributor to this bias. An updated configuration of UK Earth System Model (UKESM) version 1, UKESM1.1, has been developed with the aim of reducing the historical cold bias in this model. Changes implemented include an improved representation of SO2 dry deposition, along with several other smaller modifications to the aerosol scheme and a retuning of some uncertain parameters of the fully coupled Earth system model. The Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, a six-member historical ensemble and a subset of future scenario simulations are completed. In addition, the total anthropogenic effective radiative forcing (ERF), its components and the effective and transient climate sensitivities are also computed. The UKESM1.1 preindustrial climate is warmer than UKESM1 by up to 0.75 K, and a significant improvement in the historical GMST record is simulated, with the magnitude of the cold bias reduced by over 50 %. The warmer climate increases ocean heat uptake in the Northern Hemisphere oceans and reduces Arctic sea ice, which is in better agreement with observations. Changes to the aerosol and related cloud properties are a driver of the improved GMST simulation despite only a modest reduction in the magnitude of the negative aerosol ERF (which increases by +0.08 W m−2). The total anthropogenic ERF increases from 1.76 W m−2 in UKESM1 to 1.84 W m−2 in UKESM1.1. The effective climate sensitivity (5.27 K) and transient climate response (2.64 K) remain largely unchanged from UKESM1 (5.36 and 2.76 K respectively).

Comparison of the carbon cycle and climate response to artificial ocean alkalinization and solar radiation modification

Carbon dioxide removal and solar radiation modification (SRM) are two classes of proposed climate intervention methods. A thorough understanding of climate system response to these methods calls for a good understanding of the carbon cycle response. In this study, we used an Earth system model to examine the response of global climate and carbon cycle to artificial ocean alkalinization (AOA), a method of CO2 removal, and reduction in solar irradiance that represents the overall effect of solar radiation modification. In our simulations, AOA is applied uniformly over the global ice-free ocean under the RCP8.5 scenario to bring down atmospheric CO2 to the level of RCP4.5, and SRM is applied uniformly over the globe under the RCP8.5 scenario to bring down global mean surface temperature to the level of RCP4.5. Our simulations show that with the same goal of temperature stabilization, AOA and SRM cause fundamentally different perturbations of the ocean and land carbon cycle. By the end of the 21st century, relative to the simulation of RCP8.5, AOA-induced changes in ocean carbonate chemistry enhances global oceanic CO2 uptake by 983 PgC and increases global mean surface ocean pH by 0.42. Meanwhile, AOA reduces land CO2 uptake by 79 PgC and reduces atmospheric CO2 concentration by 426 × 10−6. By contrast, relative to the simulation of RCP8.5, SRM has a minor effect on the oceanic CO2 uptake and ocean acidification. SRM-induced cooling enhances land CO2 uptake by 140 PgC and reduces atmospheric CO2 concentration by 63 × 10−6. A sudden termination of SRM causes a rate of temperature change that is much larger than that of RCP8.5. A sudden termination of AOA causes a rate of temperature change that is comparable to that of RCP8.5 and a rate of ocean acidification that is much larger than that of RCP8.5.

Two Distinct Phases of North Atlantic Eastern Subpolar Gyre and Warming Hole Evolution under Global Warming

Abstract The North Atlantic subpolar gyre (SPG) plays a crucial role in determining the regional ocean surface temperature (SST), which has profound implications on the surrounding continental and coastal climate. Here, we analyze the Max Planck Institute-Grand Ensemble global warming experiments and show that the SPG can evolve in two distinct phases under continuous global warming. In the first phase, as the global mean surface temperature approaches 2-K warming, the eastern SPG intensifies in combination with a weakening Atlantic meridional overturning circulation (AMOC), accompanied by a cooling of subpolar North Atlantic SST, known as the warming hole. The associated oceanic fingerprint matches with the observations over the last 15 years, where an intensification and cooling of the eastern SPG is related to salinity reduction at the eastern side of the SPG. However, for further warming beyond 2 K, in spite of a continuous decline in the AMOC, a northward shift of the mean zonal wind extends the subtropical gyre northward with an associated disruption of the eastern SPG intensification, resulting in the cessation of the warming hole. Therefore, a shift from the initially dominating oceanic drivers to the atmospheric driver results into a two-phase evolution of the North Atlantic Ocean SPG circulation and the associated SST under continuous global warming.