A Solar Irradiance Climate Data Record

Coddington, O.; Lean, J.; Pilewskie, P.; Snow, M.; Lindholm, D. M.

doi:10.1175/bams-d-14-00265.1

Cited by 294 publications

(343 citation statements)

References 53 publications

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“…1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 In Fig. 2, the apparently less regular solar-cycle reconstruction by NRLSSI2 between 1940 and 1960 is most likely caused by the transition from one sunspot record to another in that model (see Coddington et al, 2016). Figure 3 compares our CMIP6 dataset with the observational SOLID composite (see description above) and some direct SSI satellite observations.…”

Section: Solar Spectral Irradiance (Ssi)mentioning

confidence: 95%

“…Coddington et al (2016) describe the model algorithm, the uncertainty estimation approach, and comparisons to observations in detail. Please note that our version differs slightly from the one published by Coddington et al (2016) by using a different scaling factor between the sunspot area as measured by the Royal Greenwich Observatory (from 1874 to 1976) and the NOAA/USAF Solar Observing Optical Network (SOON) since 1966. The future release of NRLSSI2 will use the same scaling factor as the version we use.…”

Section: Nrltsi2 and Nrlssi2mentioning

confidence: 99%

“…In this context, and for the time being, the most reasonable approach (in a maximum-likelihood sense) consists of averaging their reconstructions, weighted by their uncertainty. Since, in addition, we are lacking uncertainties that can be meaningfully compared, our current recommendation is to simply take the arithmetic mean of the two model datasets: (i) the empirical model NRLTSI2 and NRLSSI2 (Coddington et al, 2016) and (ii) the semi-empirical model SATIRE (Yeo et al, 2014;Krivova et al, 2010). Note that multimodel averaging is a widely used practice in climate modeling (e.g., Smith et al, 2013).…”

Section: Cmip6-recommended Solar Irradiance Forcingmentioning

confidence: 99%

“…All climate model intercomparison studies relied so far on the NRLSSI1 dataset (Lean, 2000). However, it is becoming increasingly evident that its solar-cycle variability in the UV part of the spectrum may be too low compared to updated and more recent SSI reconstructions by models such as NRLSSI2 (Coddington et al, 2016) and SATIRE (Yeo et al, 2014). Recent studies have emphasized the sensitivity to UV forcing changes due to top-down effects (Ermolli et al, 2013;Langematz et al, 2013;Thieblemont et al, 2015;Maycock et al, 2015;Ball et al, 2016), thereby stressing the need for a state-of-the-art representation of the SSI, and in particular the UV band, in the CMIP6 solar forcing recommendation.…”

Section: Introductionmentioning

confidence: 99%

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Solar forcing for CMIP6 (v3.2)

et al. 2017

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Abstract. This paper describes the recommended solar forcing dataset for CMIP6 and highlights changes with respect to CMIP5. The solar forcing is provided for radiative properties, namely total solar irradiance (TSI), solar spectral irradiance (SSI), and the F10.7 index as well as particle forcing, including geomagnetic indices Ap and Kp, and ionization rates to account for effects of solar protons, electrons, and galactic cosmic rays. This is the first time that a recommendation for solar-driven particle forcing has been provided for a CMIP exercise. The solar forcing datasets are provided at daily and monthly resolution separately for the CMIP6 preindustrial control, historical (1850CMIP6 preindustrial control, historical ( -2014, and future (2015-2300) simulations. For the preindustrial control simulation, both constant and time-varying solar forcing components are provided, with the latter including variability on 11-year and shorter timescales but no long-term changes. For the future, we provide a realistic scenario of what solar behavior could be, as well as an additional extreme Maunderminimum-like sensitivity scenario. This paper describes the forcing datasets and also provides detailed recommendations as to their implementation in current climate models.For the historical simulations, the TSI and SSI time series are defined as the average of two solar irradiance models that are adapted to CMIP6 needs: an empirical onePublished by Copernicus Publications on behalf of the European Geosciences Union. A new and lower TSI value is recommended: the contemporary solar-cycle average is now 1361.0 W m −2 . The slight negative trend in TSI over the three most recent solar cycles in the CMIP6 dataset leads to only a small global radiative forcing of −0.04 W m −2 . In the 200-400 nm wavelength range, which is important for ozone photochemistry, the CMIP6 solar forcing dataset shows a larger solar-cycle variability contribution to TSI than in CMIP5 (50 % compared to 35 %).We compare the climatic effects of the CMIP6 solar forcing dataset to its CMIP5 predecessor by using timeslice experiments of two chemistry-climate models and a reference radiative transfer model. The differences in the long-term mean SSI in the CMIP6 dataset, compared to CMIP5, impact on climatological stratospheric conditions (lower shortwave heating rates of −0.35 K day −1 at the stratopause), cooler stratospheric temperatures (−1.5 K in the upper stratosphere), lower ozone abundances in the lower stratosphere (−3 %), and higher ozone abundances (+1.5 % in the upper stratosphere and lower mesosphere). Between the maximum and minimum phases of the 11-year solar cycle, there is an increase in shortwave heating rates (+0.2 K day −1 at the stratopause), temperatures (∼ 1 K at the stratopause), and ozone (+2.5 % in the upper stratosphere) in the tropical upper stratosphere using the CMIP6 forcing dataset. This solar-cycle response is slightly larger, but not statistically significantly different from that for the CMIP5 forcing dataset.CMIP6 models wi...

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Section: Solar Spectral Irradiance (Ssi)mentioning

confidence: 95%

Section: Nrltsi2 and Nrlssi2mentioning

confidence: 99%

Section: Cmip6-recommended Solar Irradiance Forcingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solar forcing for CMIP6 (v3.2)

et al. 2017

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“…The primary factors responsible for the slightly smaller rise in ΔT (black numbers, Fig. 2.14) compared to AAWR over 1998-2012 is the tendency of the climate system to be in a more La Niña like state during the latter half of this period of time 31 (Kosaka and Xie 2013) and a relatively small value of total solar irradiance during the most recent solar max cycle (Coddington et al 2016). Our simulations, which include Kasatochi, Sarychev and Nabro, suggest these recent minor volcanic eruptions played only a miniscule role (~0.0018 °C/decade cooling) over this period.…”

Section: Global Warming Hiatusmentioning

confidence: 99%

Forecasting Global Warming

et al. 2017

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This chapter provides an overview of the factors that will govern the rise in global mean surface temperature (GMST) over the rest of this century. We evaluate GMST using two approaches: analysis of archived output from atmospheric, oceanic general circulation models (GCMs) and calculations conducted using a computational framework developed by our group, termed the Empirical Model of Global Climate (EM-GC). Comparison of the observed rise in GMST over the past 32 years with GCM output reveals these models tend to warm too quickly, on average by about a factor of two. Most GCMs likely represent climate feedback in a manner that amplifies the radiative forcing of climate due to greenhouse gases (GHGs) too strongly. The GCM-based forecast of GMST over the rest of the century predicts neither the target (1.5 °C) nor upper limit (2.0 °C warming) of the Paris Climate Agreement will be achieved if GHGs follow the trajectories of either the Representative Concentration Pathway (RCP) 4.5 or 8.5 scenarios. Conversely, forecasts of GMST conducted in the EM-GC framework indicate that if GHGs follow the RCP 4.5 trajectory, there is a reasonably good probability (~75 %) the Paris target of 1.5 °C warming will be achieved, and an excellent probability (>95 %) global warming will remain below 2.0 °C. Uncertainty in the EM-GC forecast of GMST is primarily caused by the ability to simulate past climate for various combinations of parameters that represent climate feedback and radiative forcing due to aerosols, which provide disparate projections of future warming.

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Isolating the roles of different forcing agents in global stratospheric temperature changes using model integrations with incrementally added single forcings

et al. 2016

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Satellite instruments show a cooling of global stratospheric temperatures over the whole data record (1979-2014). This cooling is not linear, and includes two descending steps in the early 1980s and mid-1990s. The 1979-1995 period is characterized by increasing concentrations of ozone depleting substances (ODS) and by the two major volcanic eruptions of El Chichón (1982) and Mount Pinatubo (1991). The 1995-present period is characterized by decreasing ODS concentrations and by the absence of major volcanic eruptions. Greenhouse gas (GHG) concentrations increase over the whole time period. In order to isolate the roles of different forcing agents in the global stratospheric temperature changes, we performed a set of AMIP-style simulations using the NASA Goddard Earth Observing System Chemistry-Climate Model (GEOSCCM). We find that in our model simulations the cooling of the stratosphere from 1979 to present is mostly driven by changes in GHG concentrations in the middle and upper stratosphere and by GHG and ODS changes in the lower stratosphere. While the cooling trend caused by increasing GHGs is roughly constant over the satellite era, changing ODS concentrations cause a significant stratospheric cooling only up to the mid-1990s, when they start to decrease because of the implementation of the Montreal Protocol. Sporadic volcanic events and the solar cycle have a distinct signature in the time series of stratospheric temperature anomalies but do not play a statistically significant role in the long-term trends from 1979 to 2014. Several factors combine to produce the step-like behavior in the stratospheric temperatures: in the lower stratosphere, the flattening starting in the mid 1990's is due to the decrease in ozone depleting substances; Mount Pinatubo and the solar cycle cause the abrupt steps through the aerosol-associated warming and the volcanically induced ozone depletion. In the middle and upper stratosphere, changes in solar irradiance are largely responsible for the step-like behavior of global temperatures anomalies, together with volcanically induced ozone depletion and water vapor increases in the post-Pinatubo years.

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A Solar Irradiance Climate Data Record

Cited by 294 publications

References 53 publications

Solar forcing for CMIP6 (v3.2)

Solar forcing for CMIP6 (v3.2)

Forecasting Global Warming

Isolating the roles of different forcing agents in global stratospheric temperature changes using model integrations with incrementally added single forcings

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