Detecting sulphate aerosol geoengineering with different methods

Abstract Sulphate aerosol injection has been widely discussed as a possible way to engineer future climate. Monitoring it would require detecting its effects amidst internal variability and in the presence of other external forcings. We investigate how the use of different detection methods and filtering techniques affects the detectability of sulphate aerosol geoengineering in annual-mean global-mean near-surface air temperature. This is done by assuming a future scenario that injects 5 Tg yr−1 of sulphur dioxide into the stratosphere and cross-comparing simulations from 5 climate models. 64% of the studied comparisons would require 25 years or more for detection when no filter and the multi-variate method that has been extensively used for attributing climate change are used, while 66% of the same comparisons would require fewer than 10 years for detection using a trend-based filter. This highlights the high sensitivity of sulphate aerosol geoengineering detectability to the choice of filter. With the same trend-based filter but a non-stationary method, 80% of the comparisons would require fewer than 10 years for detection. This does not imply sulphate aerosol geoengineering should be deployed, but suggests that both detection methods could be used for monitoring geoengineering in global, annual mean temperature should it be needed.

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Seasonal-to-Interannual Prediction Skills of Near-Surface Air Temperature in the CMIP5 Decadal Hindcast Experiments

Journal of Climate ◽

10.1175/jcli-d-15-0182.1 ◽

2016 ◽

Vol 29 (4) ◽

pp. 1511-1527 ◽

Cited By ~ 9

Author(s):

Jung Choi ◽

Seok-Woo Son ◽

Yoo-Geun Ham ◽

June-Yi Lee ◽

Hye-Mi Kim

Keyword(s):

Air Temperature ◽

Climate Models ◽

Southern Oscillation ◽

Surface Air Temperature ◽

Equatorial Pacific ◽

Prediction Skill ◽

Good Opportunity ◽

Near Surface ◽

Global Mean

Abstract This study explores the seasonal-to-interannual near-surface air temperature (TAS) prediction skills of state-of-the-art climate models that were involved in phase 5 of the Coupled Model Intercomparison Project (CMIP5) decadal hindcast/forecast experiments. The experiments are initialized in either November or January of each year and integrated for up to 10 years, providing a good opportunity for filling the gap between seasonal and decadal climate predictions. The long-lead multimodel ensemble (MME) prediction is evaluated for 1981–2007 in terms of the anomaly correlation coefficient (ACC) and mean-squared skill score (MSSS), which combines ACC and conditional bias, with respect to observations and reanalysis data, paying particular attention to the seasonal dependency of the global-mean and equatorial Pacific TAS predictions. The MME shows statistically significant ACCs and MSSSs for the annual global-mean TAS for up to two years, mainly because of long-term global warming trends. When the long-term trends are removed, the prediction skill is reduced. The prediction skills are generally lower in boreal winters than in other seasons regardless of lead times. This lack of winter prediction skill is attributed to the failure of capturing the long-term trend and interannual variability of TAS over high-latitude continents in the Northern Hemisphere. In contrast to global-mean TAS, regional TAS over the equatorial Pacific is predicted well in winter. This is mainly due to a successful prediction of the El Niño–Southern Oscillation (ENSO). In most models, the wintertime ENSO index is reasonably well predicted for at least one year in advance. The sensitivity of the prediction skill to the initialized month and method is also discussed.

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Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

10.5194/egusphere-egu21-9722 ◽

2021 ◽

Author(s):

Thordis Thorarinsdottir ◽

Jana Sillmann ◽

Marion Haugen ◽

Nadine Gissibl ◽

Marit Sandstad

Keyword(s):

Air Temperature ◽

Model Evaluation ◽

Climate Models ◽

Climate Model ◽

Mean Squared Error ◽

Surface Air Temperature ◽

Cmip5 Models ◽

Model Output ◽

Intergovernmental Panel ◽

Near Surface

Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important&#160;as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.

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Gaussian Process emulation of multi-model ice sheet and glacier projections

10.5194/egusphere-egu21-14561 ◽

2021 ◽

Author(s):

Tamsin Edwards ◽

Keyword(s):

Gaussian Process ◽

Sea Level Rise ◽

Sea Level ◽

Climate Models ◽

Ice Sheet ◽

Surface Air Temperature ◽

Model Parameters ◽

Gaussian Process Emulation ◽

Model Ensembles ◽

Global Mean

The land ice contribution to global mean sea level rise has not yet been predicted with ice sheet and glacier models for the latest set of socio-economic scenarios (SSPs), nor with coordinated exploration of uncertainties arising from the various computer models involved. Two recent international projects (ISMIP6 and GlacierMIP) generated a large suite of projections using multiple models, but mostly used previous generation scenarios and climate models, and could not fully explore known uncertainties. Here we estimate probability distributions for these projections for the SSPs using Gaussian Process emulation of the ice sheet and glacier model ensembles. We model the sea level contribution as a function of global mean surface air temperature forcing and (for the ice sheets) model parameters, with the 'nugget' allowing for multi-model structural uncertainty. Approximate independence of ice sheet and glacier models is assumed, because a given model responds very differently under different setups (such as initialisation). We find that limiting global warming to 1.5&#176;C would halve the land ice contribution to 21st century sea level rise, relative to current emissions pledges: the median decreases from 25 to 13 cm sea level equivalent (SLE) by 2100. However, the Antarctic contribution does not show a clear response to emissions scenario, due to competing processes of increasing ice loss and snowfall accumulation in a warming climate. However, under risk-averse (pessimistic) assumptions for climate and Antarctic ice sheet model selection and ice sheet model parameter values, Antarctic ice loss could be five times higher, increasing the median land ice contribution to 42 cm SLE under current policies and pledges, with the 95th percentile exceeding half a metre even under 1.5&#176;C warming. Gaussian Process emulation can therefore be a powerful tool for estimating probability density functions from multi-model ensembles and testing the sensitivity of the results to assumptions.

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The Sensitivity of the Hydrological Cycle to Internal Climate Variability versus Anthropogenic Climate Change

Journal of Climate ◽

10.1175/jcli-d-15-0408.1 ◽

2016 ◽

Vol 29 (10) ◽

pp. 3661-3673 ◽

Cited By ~ 8

Author(s):

Ryan J. Kramer ◽

Brian J. Soden

Keyword(s):

Water Vapor ◽

Time Scales ◽

Climate Models ◽

Hydrological Cycle ◽

Internal Variability ◽

Radiative Cooling ◽

Surface Warming ◽

Internal Climate Variability ◽

Radiative Processes ◽

Global Mean

Abstract In response to rising CO2 concentrations, climate models predict that globally averaged precipitation will increase at a much slower rate than water vapor. However, some observational studies suggest that global-mean precipitation and water vapor have increased at similar rates. While the modeling results emphasize changes at multidecadal time scales where the anthropogenic signal dominates, the shorter observational record is more heavily influenced by internal variability. Whether the physical constraints on the hydrological cycle fundamentally differ between these time scales is investigated. The results of this study show that while global-mean precipitation is constrained by radiative cooling on both time scales, the effects of CO2 dominate on multidecadal time scales, acting to suppress the increase in radiative cooling with warming. This results in a smaller precipitation change compared to interannual time scales where the effects of CO2 forcing are small. It is also shown that intermodel spread in the response of atmospheric radiative cooling (and thus global-mean precipitation) to anthropogenically forced surface warming is dominated by clear-sky radiative processes and not clouds, while clouds dominate under internal variability. The findings indicate that the sensitivity of the global hydrological cycle to surface warming differs fundamentally between internal variability and anthropogenically forced changes and this has important implications for interpreting observations of the hydrological sensitivity.

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An “Observational Large Ensemble” to Compare Observed and Modeled Temperature Trend Uncertainty due to Internal Variability

Journal of Climate ◽

10.1175/jcli-d-16-0905.1 ◽

2017 ◽

Vol 30 (19) ◽

pp. 7585-7598 ◽

Cited By ~ 24

Author(s):

Karen A. McKinnon ◽

Andrew Poppick ◽

Etienne Dunn-Sigouin ◽

Clara Deser

Keyword(s):

North America ◽

Surface Air Temperature ◽

Internal Variability ◽

Climate Change Signal ◽

Climate Projections ◽

Large Ensemble ◽

Near Surface ◽

Temperature Trends ◽

Irreducible Uncertainty ◽

Statistical Resampling

Abstract Estimates of the climate response to anthropogenic forcing contain irreducible uncertainty due to the presence of internal variability. Accurate quantification of this uncertainty is critical for both contextualizing historical trends and determining the spread of climate projections. The contribution of internal variability to uncertainty in trends can be estimated in models as the spread across an initial condition ensemble. However, internal variability simulated by a model may be inconsistent with observations due to model biases. Here, statistical resampling methods are applied to observations in order to quantify uncertainty in historical 50-yr (1966–2015) winter near-surface air temperature trends over North America related to incomplete sampling of internal variability. This estimate is compared with the simulated trend uncertainty in the NCAR CESM1 Large Ensemble (LENS). The comparison suggests that uncertainty in trends due to internal variability is largely overestimated in LENS, which has an average amplification of variability of 32% across North America. The amplification of variability is greatest in the western United States and Alaska. The observationally derived estimate of trend uncertainty is combined with the forced signal from LENS to produce an “Observational Large Ensemble” (OLENS). The members of OLENS indicate the range of observationally constrained, spatially consistent temperature trends that could have been observed over the past 50 years if a different sequence of internal variability had unfolded. The smaller trend uncertainty in OLENS suggests that is easier to detect the historical climate change signal in observations than in any given member of LENS.

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Reduced complexity model intercomparison project phase 1: Protocol, results and initial observations

10.5194/gmd-2019-375 ◽

2020 ◽

Cited By ~ 1

Author(s):

Zebedee R. J. Nicholls ◽

Malte Meinshausen ◽

Jared Lewis ◽

Robert Gieseke ◽

Dietmar Dommenget ◽

...

Keyword(s):

Radiative Forcing ◽

Climate Models ◽

Phase 1 ◽

Coupled Model ◽

Surface Air Temperature ◽

Internal Variability ◽

Second Phase ◽

Model Intercomparison ◽

Intercomparison Project ◽

Reduced Complexity

Abstract. Here we present results from the first phase of the Reduced Complexity Model Intercomparison Project (RCMIP). RCMIP is a systematic examination of reduced complexity climate models (RCMs), which are used to complement and extend the insights from more complex Earth System Models (ESMs), in particular those participating in the Sixth Coupled Model Intercomparison Project (CMIP6). In Phase 1 of RCMIP, with 14 participating models namely ACC2, AR5IR (2 and 3 box versions), CICERO-SCM, ESCIMO, FaIR, GIR, GREB, Hector, Held et al. two layer model, MAGICC, MCE, OSCAR and WASP, we highlight the structural differences across various RCMs and show that RCMs are capable of reproducing global-mean surface air temperature (GSAT) changes of ESMs and historical observations. We find that some RCMs are capable of emulating the GSAT response of CMIP6 models to within a root-mean square error of 0.2 °C (of the same order of magnitude as ESM internal variability) over a range of scenarios. Running the same model configurations for both RCP and SSP scenarios, we see that the SSPs exhibit higher effective radiative forcing throughout the second half of the 21st Century. Comparing our results to the difference between CMIP5 and CMIP6 output, we find that the change in scenario explains approximately 46 % of the increase in higher end projected warming between CMIP5 and CMIP6. This suggests that changes in ESMs from CMIP5 to CMIP6 explain the rest of the increase, hence the higher climate sensitivities of available CMIP6 models may not be having as large an impact on GSAT projections as first anticipated. A second phase of RCMIP will complement RCMIP Phase 1 by exploring probabilistic results and emulation in more depth to provide results available for the IPCC's Sixth Assessment Report author teams.

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An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

The Cryosphere Discussions ◽

10.5194/tcd-6-3317-2012 ◽

2012 ◽

Vol 6 (4) ◽

pp. 3317-3348 ◽

Cited By ~ 2

Author(s):

C. Brutel-Vuilmet ◽

M. Ménégoz ◽

G. Krinner

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Air Temperature ◽

Linear Correlation ◽

Large Scale ◽

Climate Models ◽

Surface Air Temperature ◽

Warming Trend ◽

Snow Cover Extent ◽

Global Mean

Abstract. The 20th century seasonal Northern Hemisphere land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a~reduced spring snow cover extent over the 1979–2005 is underestimated. We show that this is linked to the simulated Northern Hemisphere extratropical land warming trend over the same period, which is underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between hemispheric seasonal spring snow cover extent and boreal large-scale annual mean surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear correlation between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the sensitivity of the Northern Hemisphere spring snow cover to global mean surface air temperature changes is underestimated at present because of the underestimate of the boreal land temperature change amplification.

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Simulation of the mid-Pliocene Warm Period using HadGEM3-GC31-LL: Pliocene climate relative to the pre-industrial era, previous model versions, other climate models and proxy data

10.5194/egusphere-egu21-7707 ◽

2021 ◽

Author(s):

Charles Williams ◽

Daniel Lunt ◽

Alistair Sellar ◽

William Roberts ◽

Robin Smith ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Climate Model ◽

High Sensitivity ◽

Warm Period ◽

Future Climate ◽

Future Climate Change ◽

Last Interglacial ◽

Proxy Data ◽

Global Mean

To better understand the processes contributing to future climate change, palaeoclimate model simulations are an important tool because they allow testing of the models&#8217; ability to simulate very different climates than that of today.&#160; As part of CMIP6/PMIP4, the latest version of the UK&#8217;s physical climate model, HadGEM3-GC31-LL (hereafter, for brevity, HadGEM3), was recently used to simulate the mid-Holocene (~6 ka) and Last Interglacial (~127 ka) simulations and the results were compared to the preindustrial era, previous versions of the same model and proxy data (see Williams et al. 2020, Climate of the Past).&#160; Here, we use the same model to go further back in time, presenting the results from the mid-Pliocene Warm Period (~3.3 to 3 ma, hereafter the &#8220;Pliocene&#8221; for brevity).&#160; This period is of particular interest when it comes to projections of future climate change under various scenarios of CO2 emissions, because it is the most recent time in Earth&#8217;s history when CO2 levels were roughly equivalent to today.&#160; In response, albeit due to slower mechanisms than today&#8217;s anthropogenic fossil fuel driven-change, during the Pliocene global mean temperatures were 2-3&#176;C higher than today, more so at the poles.&#160;Here, we present results from the HadGEM3 Pliocene simulation.&#160; The model is responding to the Pliocene boundary conditions in a manner consistent with current understanding and existing literature.&#160; When compared to the preindustrial era, global mean temperatures are currently ~5&#176;C higher, with the majority of warming coming from high latitudes due to polar amplification from a lack of sea ice.&#160; Relative to other models within the Pliocene Modelling Intercomparison Project (PlioMIP), this is the 2nd warmest model, with the majority of others only showing up to a 4.5&#176;C increase and many a lot less.&#160; This is consistent with the relatively high sensitivity of HadGEM3, relative to other CMIP6-class models.&#160; When compared to a previous generation of the same UK model, HadCM3, similar patterns of both surface temperature and precipitation changes are shown (relative to preindustrial).&#160; Moreover, when the simulations are compared to proxy data, the results suggest that the HadGEM3 Pliocene simulation is closer to the reconstructions than its predecessor.

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What does global mean temperature tell us about local climate?

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2014.0426 ◽

2015 ◽

Vol 373 (2054) ◽

pp. 20140426 ◽

Cited By ~ 32

Author(s):

Rowan Sutton ◽

Emma Suckling ◽

Ed Hawkins

Keyword(s):

Climate Change ◽

Climate Model ◽

Surface Air Temperature ◽

Internal Variability ◽

Climate Impacts ◽

Climate Projections ◽

Local Climate ◽

Forced Responses ◽

The Relationship ◽

Global Mean

The subject of climate feedbacks focuses attention on global mean surface air temperature (GMST) as the key metric of climate change. But what does knowledge of past and future GMST tell us about the climate of specific regions? In the context of the ongoing UNFCCC process, this is an important question for policy-makers as well as for scientists. The answer depends on many factors, including the mechanisms causing changes, the timescale of the changes, and the variables and regions of interest. This paper provides a review and analysis of the relationship between changes in GMST and changes in local climate, first in observational records and then in a range of climate model simulations, which are used to interpret the observations. The focus is on decadal timescales, which are of particular interest in relation to recent and near-future anthropogenic climate change. It is shown that GMST primarily provides information about forced responses, but that understanding and quantifying internal variability is essential to projecting climate and climate impacts on regional-to-local scales. The relationship between local forced responses and GMST is often linear but may be nonlinear, and can be greatly complicated by competition between different forcing factors. Climate projections are limited not only by uncertainties in the signal of climate change but also by uncertainties in the characteristics of real-world internal variability. Finally, it is shown that the relationship between GMST and local climate provides a simple approach to climate change detection, and a useful guide to attribution studies.

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Evaluation of MM5 and HadGEM3-RA hindcast in the CORDEX East Asia Phase Ⅱ: near-surface air temperature

10.5194/egusphere-egu2020-15161 ◽

2020 ◽

Author(s):

Seok-Woo Shin ◽

Dong-Hyun Cha ◽

Taehyung Kim ◽

Gayoung Kim ◽

Changyoung Park ◽

...

Keyword(s):

East Asia ◽

Air Temperature ◽

Climate Models ◽

Extreme Values ◽

Regional Climate ◽

Surface Air Temperature ◽

Regional Climate Models ◽

Near Surface ◽

The Mean ◽

Cordex East Asia

Extreme temperature can have a devastating impact on the ecological environment (i.e., human health and crops) and the socioeconomic system. To adapt to and cope with the rapidly changing climate, it is essential to understand the present climate and to estimate the future change in terms of temperature. In this study, we evaluate the characteristics of near-surface air temperature (SAT) simulated by two regional climate models (i.e., MM5 and HadGEM3-RA) over East Asia, focusing on the mean and extreme values. To analyze extreme climate, we used the indices for daily maximum (Tmax) and minimum (Tmin) temperatures among the developed Expert Team on Climate Change Detection and Indices (ETCCDI) indices. In the results of the CORDEX-East Asia phase &#8544;, the mean and extreme values of SAT for DJF (JJA) tend to be colder (warmer) than observation data over the East Asian region. In those of CORDEX-East Asia phase &#8545;, the mean and extreme values of SAT for DJF and JJA have warmer than those of the CORDEX-East Asia phase &#8544; except for those of HadGEM3-RA for DJF. Furthermore, the Extreme Temperature Range (ETR, maximum value of Tmax - minimum value of Tmin) of CORDEX-East Asia phase &#8544; data, which are significantly different from those of observation data, are reduced in that of CORDEX-East Asia phase &#8545;. Consequently, the high-resolution regional climate models play a role in the improvement of the cold bias having the relatively low-resolution ones. To understand the reasons for the improved and weak points of regional climate models, we investigated the atmospheric field (i.e., flow, air mass, precipitation, and radiation) influencing near-surface air temperature. Model performances for SAT over East Asia were influenced by the expansion of the western North Pacific subtropical high and the location of convective precipitation in JJA and by the contraction of the Siberian high, the spatial distribution of snowfall and associated upwelling longwave radiation in DJF.

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