IPCC Summary for Policymakers
IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
This report considers new evidence of CC based on climate system, paleoclimate and theoretical studies via models. Working Group 1 contributions to AR4.
Section E.8 Climate Stabilisation, CC Commitment and irreversibility
- Cumulative CO2 emissions determine global mean surface warming by late 21st century +.
- Even if CO2 emissions are stopped, CC effects will persist for centuries.
- Cumulative total emissions of CO2 and global mean temperature response are linearly related.
- Limiting anthropogenic-caused CO2 emissions warming to less than 2 degrees with less than 33, 50 or 66% probability requires CO2 emissions to stay between
- For 33% 0 and 1670gtC (gigatons of Carbon)
- For 50% 0 and 1210GtC
- For 66% 0 and 1000GtC
- Reduce these amounts by 9000gtc, 820gtc and 790gtc given that many non-CO2atmospheric gases absorb in the infrared and contribute to climate forcing,
- so talking only CO2 levels must be decreased to account for other gases contributing to climate warming.
- in 2011 515GtC were already emitted.
- To reach lower warming target we need less Co2 cumulative emissions.
- Lowering non-Co2 greenhouse gases, areosols and permafrost gases also needed.
- linear relation between CO2 total cumulative emissions, multi-model results from (RCP until 2011) à decadal means in dots
- Multi-model spread over four RCP scenarios à pink fades with less models available after RCP 8.5
- Model CMIP5 simulate 1% increase in CO2 missions per yearà grey line and area
- 1% per year CO2 emissions account for lower warming than RCP models accounting for additional gases (nonCO2) that cause warming.
- What is RCP: Representative concentration pathway
RCP2.6 very low forcing levels (so, warming) given mitigation
RCP4.5 and 5 stabilisation scenarios
RCP8 very high greenhouse gas emissions
- RCP provides data sets for land use change, sector based emissions of air pollutants and specifies annual greenhouse has concentration and anthropogenic emissions to 2100. à do not cover full range of emissions (no areosols)
Quantifying uncertainties in global and regional temperature change using observational estimates: HadCRUT4 data set
Cite as: Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones (2012), Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set, J. Geophys. Res., 117, D08101.
Observational near-surface air temperature and sea-surface temperature combined produce HadCRUT4, which is a data set of global regional temperature evolution from 1850.
Linear trends in temperature anomalies are approx. 0,07 degrees per decade from 1901 to 2010, 0,17degrees per decade 1979 to 2010 globally, with nothern southern hemisepheric trends 0,08/0,07 degrees per decade 1901-2010 and 0.24/0.10 for 1979-2010.
Data agree with other prominent near-surface temperature analysis.
Difference between datasets can be caused by inclusion of additional data, quality control methods etc.
Difference in temperature analyses from various approaches: structural uncertainty, i.e. uncertainty in temperature analysis coming from choice of methodology! –> overcome by more data
What’s the level of certainty about temperature evolution observed in given observational analysis?
measurement error and bias model: includes descriptions of land –> station homogenization uncertainty, bias related uncertainties from urbanization, sensor exposure etc.
sensitivity analysis of effect of possible biases in observational near-suface temperature record is increased by changing methods used to present uncertainties, namely by sampling of likely surface temperature anomalies by different ensemble members
à uncertainty model only takes into account uncertainties that have already been identified in the construction of the model. Unidentified sources, like structural uncertainty (coming from methodology) are not taken into account by the model.
Systematic bias in observation can lead to patterns of uncertainty in the observational data set. –> uncertainties in observational data have two important factors:
- when uncertainties are correlated, they do not cancel in the computation of averages of the data.
- Gradual change in network of observation where the biases permeate lead to low frequency component of bias in the time series derived from the data.
Distribution of likely measurement biases –> take feasible biases and adjust dataset by subtracting bias. (reiterate for different measurement biases)
Uncertainties are expressed by providing multiple realisation of data relative to temperature anomalies, to represent the possible realisations of the dataset given particular uncertainty in measurement biases. For HadCrut there are 100 realisations.
HadSST3 Ensemble data set: (SST stands for Sea Surface Temperature)
SST bias adjustment realisations: different techniques for measuring sea surface temperature à adjustments for each measurement types are combined and weighted by the fraction of measurements carried out. These adjustments are added to the tempertature anomalies to create multiple realisations of the SSTR data set representing uncertainty. –> systematic bias of individual measurements (micro-biases) is also included
CRUTEM4 ensemble data set:
Accounts for uncertainty e.g. due to urbanisation
Construct an ensemble version of CRUTEM4 by drawing error realisation that are possible from some uncertainty model and combine these error realisations with the station records à calculation of uncertainty ranges in averages arising from correlated uncertainties.
Station homogenisation adjustment error: homogenisation is the process of identification and removal of artifacts in station records, e.g. caused by changes in measurement equipment, relocation of stations in the local area etc. à small discontinuities in station records are modelled to be removed.
Station climatological normal uncertainty: nrmal uncertainty represent the uncertainty in forming calendar montly climatological average temperature over 1961 and 1990 reference period used to convert temperature into anomalies. –> This uncertainty is modelled to be correlated with a five calendar month in all years.
Exposure bias: uncertainty measurement bias on a regional to global scale arising from new varieties of measurement sensors (technical advancement).
Measurement and sampling error: random measurement error and sampling errors.
Coverage errors: uncertainty representing range of likely errors in regional averages computed form data with incomplete spatial coverage
Blending CRUTEM and HadSST3–> overlap
More uncertainty in HadCrut4 despite increased number of station included in the data setà reason: includes interdependencies of sea surface temperature measurement uncertainties arising from micro-biases in SST.
Regional time series: fewer measurements contribute to regional averages than global averages and interdependence of errors in sea surface temperature measurements are stronger for measurements that are locally close, measurements of bias related uncertainties in regional average are larger than global averages. à limited coverage uncertainty big in regional averages, because scarcity of measurements in high latitudes of northern hemisphere.
Conclusion: new data, studies of uncertainties in near-surface temperature; measurements have identified correlation structures in measurement uncertainties that translate into correlated uncertainties in derived data sets.