climateetc--2020-02-13--Plausible scenarios for climate change: 2020-2050

A range of scenarios for global mean surface temperature change between 2020 and 2050 , derived using a semi-empirical approach .
All three modes of natural climate variability – volcanoes , solar and internal variability – are expected to act in the direction of cooling during this period .
In the midst of all the angst about 1.5oC or 2.0oC warming or more , as defined relative to some mythical time when climate was alleged to be ‘ stable ’ and ( relatively ) uninfluenced by humans , we lose sight of the fact that we have a better baseline @ @ @ @ @ @ @ using ‘ now ’ as a baseline for future climate change is that we have good observations to describe the climate of ‘ now ’ .
While most of the focus of climate projections is on 2100 , the period circa 2020-2050 is of particular importance for several reasons : • It is the period for meeting UNFCCC targets for emissions reductions • Many financial and infrastructure decisions will be made on this time scale • The actual evolution of the climate over this period will influence 1 ) and 2 ) above @ @ @ @ @ @ @ impacts on decisions related to 1 ) and 2 ) .
Global climate/earth system models have little skill on decadal time scales .
To address this issue , CMIP5 and CMIP6 are conducting initialized , decadal scale simulations out to 35 years .
While I haven ’ t seen any CMIP6 decadal results yet , I do follow this literature .
Punchline is that there is some skill in simulating the Atlantic Multidecadal Oscillation ( AMO ) out to 8-10 years , but otherwise not much overall skill .
I have previously criticized @ @ @ @ @ @ @ change scenarios – instead , these simulations show the sensitivity of climate to different emissions scenarios .
They neglect scenarios of future solar variability , volcanic eruptions , and the correct phasing and amplitude of multidecadal variability associated with ocean circulations .
The argument for dismissing these factors is that they are smaller than emissions forcing .
Well , cumulatively and on decadal to multi-decadal timescales , this is not necessarily true .
And in the CMIP6 era , we now have sufficient information and understanding so that we can generate plausible scenarios @ @ @ @ @ @ @ 21st century , as well as for the AMO .
I have developed a semi-empirical approach to formulating 21st century climate change scenarios that rely only indirectly on climate models .
Multiple scenarios are generated for each driver of the forecast ( natural and anthropogenic ) , with an emphasis on plausible scenarios ( rather than extreme scenarios that can not completely be ruled out ) .
Note : in what follows , many references are cited .
I don ’ t have time now to pull together a full bibliography , but @ @ @ @ @ @ @ references .
The approach used here is to use as much as possible the new information becoming available for CMIP6 : new emission scenarios , new considerations regarding climate model sensitivity to CO .
Similar to the recent IPCC SR1.5 Report , no attempt is made to use CMIP6 Earth System Model outputs .
Following the IPCC SR1.5 , scenarios of global warming are driven by scenarios of cumulative emissions .
The individual cumulative emission scenarios between 2020 and 2050 are then translated into a global temperature increase using a range of values @ @ @ @ @ @ @ Carbon Emissions ( TCRE ) .
This approach is illustrated in the following figure : Figure 1 : CO2-induced warming as a function of cumulative emissions and TCRE .
Millar et al For the forthcoming IPCC AR6 , a new set of emissions scenarios ( SSP ) have been issued .
The 2019 World Energy Outlook Report from the International Energy Agency ( IEA ) challenges the near term SSP scenario projections through 2040 .
They examined three scenarios : a current policy scenario ( CPS ) where no new climate or energy @ @ @ @ @ @ @ stated policies scenario ( STPS ) where Paris Agreement commitments are met , and a sustainable development scenario ( SDS ) where rapid mitigation limits late 21st warming to well below 2°C .
Both the IEA CPS and STPS scenarios can be considered as business-as-usual where either current policies or current commitments continue , but no additional climate policies are adopted after that point .
Figure 2 compares the IEA fossil fuel emissions projections to scenarios being used in the IPCC AR6 .
The figure indicates that the IEA CPS emissions are between @ @ @ @ @ @ @ the IEA STPS scenario is slightly below SSP2-RCP4.5 .
Figure 2 : Annual CO2 emissions from fossil fuel and industry in CPS and STPS IEA scenarios compared to the range of baseline scenarios examined in the SSP Database , as well as a subset of the baseline and mitigation scenarios chosen for use in the forthcoming IPCC AR6 report .
Ritchie and Hausfather ( 2019 ) https : //thebreakthrough.org/issues/energy/3c-world In view of these considerations , I select a single scenario for consideration here : SSP2-4.5 .
For the timescale of this analysis ( @ @ @ @ @ @ @ between 4.5 and 6.0 , and we are not currently on the 7.0 trajectory .
Cumulative emissions for SSP2-4.5 calculated from 2020 to 2050 are reported in Table 1 for both cumulative CO and cumulative C ( carbon ) .
Cumulative C is used in calculating the transient climate response to cumulative carbon emissions ( TCRE ) ; note that 1000 GtC is the carbon content of 3667 GtCO Table 1 : Projections of cumulative CO ( GtCO ) and C concentrations ( GtC ) between 2020-2050 , for 3 SSP emissions scenarios @ @ @ @ @ @ @ reference , the IPCC SR1.5 Report assessed that amount of additional cumulative CO emissions ( 50th percentile ) from a reference period 2006-2015 to keep additional warming to within 0.5°C is 580 GtCO , and to keep additional warming to within 1.0°C is 1500 GtCO .
Translating the emissions scenarios into a global temperature increase has traditionally been conducted using global climate or earth system model simulations .
However , the CMIP6 simulations using the new SSP scenarios and their analysis are currently underway .
The recent IPCC SR1.5 Report chose to use @ @ @ @ @ @ @ cumulative carbon emissions , or TCRE , to relate global temperature change to the cumulative emissions in the SSP scenarios .
The amount of warming the world is projected to experience from emissions is approximately linearly proportional to cumulative carbon emissions ( for an overview , see Matthews et al .
2018 ) .
This relationship between temperatures and cumulative emissions is referred to as the transient climate response to cumulative carbon emissions , or TCRE .
For a give value of TCRE , we can calculate the amount of warming expected over @ @ @ @ @ @ @ of cumulative carbon emission .
The IPCC AR5 provided a likely range for TCRE of 0.8°C to 2.5°C .
Matthews et al .
( 2018 ) state that the current generation of full-complexity Earth-system models exhibits a range of TCRE values of between 0.8 and 2.4°C , with a median value of 1.6°C .
An observationally-constrained TCRE estimate gave a 5 % –95 % confidence range of 0.7 −2.0°C , with a best-estimate of 1.35 ◦C ( Gillett et al 2013 ) .
A more recent observationally-constrained estimate is provided by Lewis ( @ @ @ @ @ @ @ estimate of 1.05°C .
In view of these assessments , I select the following values of TCRE for scenarios : 1.0 , 1.35 , 1.65 , 2.0°C as constituting a range of plausible values .
Table 2 provides calculations of the amount of warming between 2020 and 2050 , based on SSP2-4.5 and four values of TCRE .
As expected from the range of TCRE values used here , there is a factor-of-two range in the amount of emissions-driven warming expected for the period 2020-2050 .
Table 2 : Warming scenarios ( oC @ @ @ @ @ @ @ based on the SSP2-4.5 cumulative emissions scenario ( GtC ) and four values of TCRE ( oC ) Scenarios of future variations/changes are presented for 2030-2050 for the following : With regards to solar scenarios for the 21st century , there are two issues : • How much total solar insolation ( TSI ) will change • How much warming , given a specific TSI .
According to the IPCC AR5 , the influence of the Sun on our climate since pre-industrial times , in terms of radiative forcing , is very small @ @ @ @ @ @ @ due to added anthropogenic greenhouse gases : 0.05 W/m2 vs. 2.29 W/m2 .
Thus , the IPCC AR5 message is that changes in solar activity are nearly negligible compared to anthropogenic forcing .
This interpretation has been challenged : • There is substantial disagreement on trends in solar activity , even in the satellite era .
Several papers in the last decade have claimed that solar activity in the second part of the 20th century was higher than any time in the past 10,000 years .
Some studies claim that the Sun could @ @ @ @ @ @ @ of the post 1850 global warming .
• The IPCC AR5 considered only the direct solar effects on global temperatures .
It has been found that over the eleven- year solar cycle the energy that enters the Earth ’ s system is of the order of 1.0–1.5 W/m2 .
This is almost an order of magnitude larger than what would be expected from solar irradiance alone , and suggests that solar activity is getting amplified atmospheric processes .
Candidate processes include : solar ultraviolet changes ; energetic particle precipitation ; atmospheric-electric-field effect on @ @ @ @ @ @ @ solar-modulated galactic cosmic rays ; large relative changes in its magnetic field ; strength of the solar wind .
• The relations between solar variations and Earth climate are many and complicated .
Most of them work locally and regionally , and many are non-linear .
Strong solar influences have been seen in the Pacific and Indian Oceans and also in the Arctic , among other regions .
As summarized by Svensmark ( 2019 ) , satellite data demonstrate that TSI varies by as much as 0.05–0.07 % over a solar cycle .
@ @ @ @ @ @ @ variation amounts to around 1 W/m2 out of a solar constant of around 1361 W/m2 .
At the surface , this is only 0.2 W/m2 , after taking geometry and albedo into account .
Model simulations and observations have shown a response of global surface temperature to TSI variations over the 11-year solar cycle of about 0.1oC ( Matthes et al .
2017 ) .
The current solar cycle 24 is the smallest sunspot cycle in 100 years and the third in a trend of diminishing sunspot cycles .
Is the Sun is @ @ @ @ @ @ @ or just a period of low solar activity ?
Many solar physicists expect the sun to move into a new minimum during the 21st century : a century-level low , although several predict a minimum comparable with the Dalton or even the Maunder Minimum .
In CMIP5 , climate projections were based on a stationary-Sun scenario , obtained by simply repeating solar cycle 23 , which ran from April 1996 to June 2008 , which is the third strongest solar cycle since 1850 .
Clearly , such a stationary scenario is not representative @ @ @ @ @ @ @ cycle-to-cycle variations and trends .
Therefore , in CMIP6 more realistic scenarios were developed for future solar activity , exhibiting variability at all timescales ( Matthes et al .
2017 ) .
Matthes et al .
present the following two scenarios ( Figure 3 ) : a reference scenario and a Maunder minimum scenario for the second half of the 21st century .
If a Maunder minimum-scale event were to occur in the 21st century , how much cooling would this cause ?
As summarized by Svensmark ( 2019 ) , a majority @ @ @ @ @ @ @ overall secular solar radiative output : since the Maunder Minimum , TSI is believed to have increased by around 1 W/m2 , which corresponds to 0.18 W/m2 at the Earth ’ s surface – this is the same magnitude of the amplitude of the 11 year solar cycle .
Jones et al .
( 2012 ) used a simple climate model to estimate that the likely reduction in the warming by 2100 from a ‘ Maunder minimum ’ scale event to be between 0.06 and 0.1 °C .
Fuelner and Rahmstorf ( 2010 @ @ @ @ @ @ @ to the Dalton and Maunder minima would cause 0.09°C and 0.26°C cooling , respectively .
Meehl et al .
( 2013 ) estimated a Maunder minimum cooling of 0.3°C .
These calculations ignored any indirect solar effects , which would arguably increase these numbers by up to a factor of 3 to 7 .
Shaviv ( 2008 ) used the oceans as a calorimeter to measure the radiative forcing variations associated with the solar cycle .
Shaviv found that the energy that enters the oceans over a solar cycle is 5–7 times larger @ @ @ @ @ @ @ , thus implying the necessary existence of an amplification mechanism .
Scafetta ( 2013 ) showed that the large climatic variability observed since the medieval times can be correctly interpreted only if the climatic effects of solar variability on the climate have been severely underestimated by the climate models by a 3 to 6 factor .
Svensmark ( 2019 ) made a comparable argument using borehole temperatures for the period since the Medieval Warm Period , finding an amplification of a factor of 5 to 7 over the warming expected from a drop @ @ @ @ @ @ @ is included of these magnitudes , then a surface temperature decrease of up to 1oC ( or even more ) from a Maunder minimum could be expected .
Three scenarios for solar variability are used here : • CMIP6 reference scenario , with factor of two amplification by solar indirect effects • CMIP6 Maunder Minimum scenario , with factor of four amplification by solar indirect effects ( note : the period 2020-2050 has lower values of TSI than the reference scenario , but the actual Minimum is in the latter half of the @ @ @ @ @ @ @ CMIP6 values of changes in TSI are ‘ eyeballed ’ from Figure 3 ( I did not download the CMIP6 solar projections ) .
I would greatly appreciate other interpretations of the values of surface cooling to infer from the CMIP6 solar scenarios .
Table 3 .
Scenarios of solar cooling ( oC ) , relative to the CMIP5 solar cycle The 21st century CMIP5 climate model simulations did not include any radiative forcing from future volcanic eruptions .
While volcanic eruptions are not predictable , a scenario of zero radiative forcing from @ @ @ @ @ @ @ poor assumption .
Further , assuming a repeat of the 20th century volcanic radiative forcing is not a very good assumption , either .
In the past decade , there have been two major paleoclimate reconstructions of volcanic eruptions in the recent millennia .
Gao et al .
( 2008 ) examined ice core records and their following reconstruction for sulfate ejection from volcanic eruptions .
A more recent reconstruction by Sigl et al .
( 2015 ) is provided below , presented in terms of global volcanic aerosol radiative forcing .
These @ @ @ @ @ @ @ level of volcanic activity since the mid 19th century .
Figure 4 : Reconstruction of global volcanic aerosol radiative forcing for the past 2500 years .
Sigl et al .
( 2015 ) Because volcanic eruptions are unpredictable events , they have generally been excluded from twenty-first century climate projection protocols .
Most recent projections either specify future volcanic forcing as zero or a constant background value .
Bethke et al .
( 2017 ) explored how sixty possible volcanic futures , consistent with ice-core records , impact climate variability projections of the @ @ @ @ @ @ @ ECS=3.2C ) under RCP4.5 .
Clustered occurrence of strong tropical eruptions has contributed to sustained cold periods such as the Little Ice Age , where the longer- term climate impacts are mediated through ocean heat content anomalies and ocean circulation changes .
Extreme volcanic activity can potentially cause extended anomalously cold periods .
Figure 5 : Annual-mean GMST .
Ensemble mean ( solid ) of VOLC ( stochastic volcanic forcing ; blue ) , VOLC-CONST ( average 1850-2000 volcanic forcing ; magenta ) and NO-VOLC ( red/orange ) with 5–95 % range ( @ @ @ @ @ @ @ ) for VOLC and NO-VOLC ; evolution of the most extreme member ( black ) .
Bethke et al .
( 2017 ) .
Based on the results of Bethke et al .
( 2017 ) , three volcanic scenarios for cooling are used , related to the decadal values shown in Figure 5 : Figure 6 .
Decadal means of GMST relative to pre-industrial .
Ensemble mean ( solid ) with 5–95 % range ( shading ) of VOLC ( blue ) and NO-VOLC ( red ) .
Table 4 shows the @ @ @ @ @ @ @ with Figure 6 .
Table 4 .
Decadal scenarios of volcanic cooling ( oC ) .
From Bethke et al .
( 2017 ) Variations in global mean surface temperature are also associated with recurrent multi-decadal internal variability associated with large-scale ocean circulations .
However , separating the internal variability from forced variability is not always straightforward owing to uncertainties in external forcing .
The multi-decadal internal variability ( 50-80 year band ) has been estimated to have a peak-to-peak amplitude of global surface temperature as high as 0.3-0.4oC ( Tung and Zhou @ @ @ @ @ @ @ half of the late 20th century warming .
DelSole et al .
( 2010 ) estimated a peak-to-peak global temperature change of 0.24oC from internal variability .
By contrast , Stolpe ( 2016 ) estimated a maximum peak-to-peak amplitude of 0.16oC .
Knutson et al .
( 2016 ) used the GFDL CM3 model , which has strong internal multidecadal variability , to identify several periods that exceed 0.5oC for global mean surface temperature , indicating that data records of ~160 years are too short for a full sampling of multi-decadal internal climate @ @ @ @ @ @ @ Multidecadal Variability as having the dominant imprint on global and Northern Hemisphere temperatures .
Identification of ENSO as a driver of global mean temperature variations or response signal remains contentious , with conflicting results .
Bhaskar et al .
( 2017 ) characterizes ENSO as a secondary driver of variations in global mean temperature , accounting for 12 % of variability over the last century , with ENSO and global mean surface temperature mutually driving each other at varied time lags .
Not taking multi-decadal variability into account in predictions of future warming @ @ @ @ @ @ @ risk of over-estimating the warming for the next two to three decades , when the Atlantic Multi-decadal Oscillation ( AMO ) is likely to shift into its cold phase .
Analysis of historical and paleoclimatic records suggest that a transition to the cold phase of the AMO is expected prior to 2050 .
Enfield and Cid-Serrano ( 2006 ) used paleoclimate reconstructions of the AMO to develop a probabilistic projection of the next AMO shift .
Figure 7 shows the probability of an AMO shift relative to the number of years since the @ @ @ @ @ @ @ shift occurred in 1995 ; hence in 2020 , it has been 23 years since the previous shift .
Figure 7 indicates that a shift to the cold phase is expected to occur within the next 15 years , with a 50 % probability of the shift occurring in the next 6 years .
Figure 7 .
Probability of an AMO regime shift relative the number of years since the last regime shift .
Source : Enfield and Cid-Serrano ( 2006 ) The timing of a shift to the AMO cold phase is @ @ @ @ @ @ @ extent on unpredictable weather variability ( Johnstone , 2020 ) .
Johnstone ’ s analysis shows that low-frequency changes in North Atlantic SSTs since 1880 are objectively identified as a series of alternating ‘ regime shifts ’ with abrupt ( ~1-year ) transitions dated to 1902 , 1926 , 1971 and 1995 ( Figure 8 ) .
In the recent historical record ( back to 1880 ) , these sharp changes punctuate longer quasi-stable periods of 24 years ( 1902-1925 ) , 45 years ( 1926-1970 ) , and 24 years ( 1971-1994 @ @ @ @ @ @ @ warmest regime on record has persisted with little net change from 1995 through 2019 ( 25 years ) .
Previous cool shifts in 1902 and 1971 shared similar -0.2°C amplitudes , following extended periods of relative warmth ( 1880-1901 ) , ( 1926-1970 ) .
A negative ( cool ) shift within a shorter time frame ( ~5 years ) might be tentatively inferred from a steep 2015 SST decline in the subpolar North Atlantic , behavior that might presage broader North Atlantic cooling based on early subpolar appearance of the most recent @ @ @ @ @ @ @ .
Annual SST anomalies in the subpolar ( blue ) and tropical ( red ) North Atlantic .
A sharp subpolar cooling is evident in 2015 .
Johnstone ( 2020 ) Guided by the above analyses , three scenarios for global temperature change associated with the AMO are presented in Table 5 .
Table 5 .
Decadal scenarios of temperature change from internal variability ( oC ) , associated with a transition to the cool phase of the Atlantic Multidecadal Oscillation .
The final integral temperature change is the sum of temperature changes @ @ @ @ @ @ @ combinations of these scenarios .
Table 6 shows extreme high and low warming scenario , plus the scenario using all of the mid range values .
Table 6 .
Integral scenarios of global mean surface temperature change for 2020-2050 .
All of the components of natural variability point to cooling during the period 2020-2050 .
Individually these terms are not expected to be large in the moderate scenarios .
However , when summed their magnitude approaches the magnitude of the warming associated with the moderate values of TCRE – 1.35 and 1.65 oC @ @ @ @ @ @ @ expected value , or TCRE is at the low end ( 1.0 to 1.35oC ) , then there could be net cooling .
The possibility and probability of 21st century decades being characterized by net cooling has been addressed by several papers .
This depends on model value of ECS , and the magnitude of the predicted natural variability .
Knutson et al .
( 2016 ) used the GFDL climate model ( relatively high value of ECS ; high internal variability ) to determine that probability of global temperature trend < 0 @ @ @ @ @ @ @ % .
Bethke et al .
( 2017 ) used NorESM ( ECS=3.2C ) with RCP4.5 .
This paper examined the combination of scenarios of internal variability and volcanic eruptions .
They found that occurrences of decades with negative GMST trend become more frequent if accounting for volcanic forcing , with the probability increasing from 10 % in NO-VOLC to more than 16 % in VOLC .
The probability of decades with negative GMST trend more than doubles from 4 % to 10 % if the analysis is limited to the first half @ @ @ @ @ @ @ RCP4.5 .
Volcanic-induced cooling becomes increasingly important in facilitating neutral or negative temperature trends on longer timescales , in conjunction with natural internal variability effects .
In summary , decade ( s ) during the period 2020-2050 with zero warming or even cooling should not be particularly surprising .
• We are starting to narrow the uncertainty in the amount of warming from emissions that we can expect out to 2050 • All three modes of natural variability – solar , volcanoes , internal variability – are expected to trend cool over the @ @ @ @ @ @ @ relative magnitudes of emissions driven warming versus natural variability , decades with no warming or even cooling are more or less plausible .
If you prefer your scenarios on the high side , you can include scenarios with RCP7.0 and TCRE=2.4oC , but these values don ’ t change the fundamental narrative presented here .
You can also add 1.2oC to the values in Table 6 , to make the numbers look higher .
But if you want plausible scenarios , look to my Table 6 , which I think bounds the range @ @ @ @ @ @ @ temperature from 2020-2050 .
But what about the 2nd half of the 21st century and 2100 ?
Uncertainties regarding emissions are much greater in the 2nd half of the 21st century .
The CMIP6 solar scenarios ( Reference and Maunder ) show more cooling in the second half of the 21st century .
Volcanic eruptions could be larger in 2nd half of 21st century ( or not ) .
After the projected cool phase of the AMO , a return to the warm phase is expected , but there is no confidence in @ @ @ @ @ @ @ AMO in 2100 .
Apart from the ‘ wild card ’ of volcanic eruptions , the big uncertainty is solar indirect effects .
Based on the literature survey that I ’ ve conducted , solar UV effects on climate seem to be at least as large as TSI effects .
A factor of 2-4 ( X TSI ) seems completely plausible to me , and serious arguments have been presented for even higher values .
I also note here that almost all estimates of ECS/TCR from observations do not include any allowances for @ @ @ @ @ @ @ Scafetta ( 2013 ) included solar indirect effects in an estimate of ECS , and determined an ECS value of 1.35 oC .
Neither the effects of AMO or solar indirect effects have been included in attribution analyses of warming since 1950 .
So why does this analysis ‘ matter ’ ?
• For those that are urgently worried about the impacts of AGW and the need to act urgently to meet deadlines related to emissions , the natural climate variability may help slow down the warming over the next few decades , @ @ @ @ @ @ @ cost effective decisions that make sense for the long term .
• Failure to anticipate and understand periods of stagnant warming or even cooling detract from the credibility of climate science and may diminish the ‘ will to act.
’ I look forward to your comments .
I encourage you to critique and check my numbers , especially related to solar .

by Judith Curry
A range of scenarios for global mean surface temperature change between 2020 and 2050, derived using a semi-empirical approach. All three modes of natural climate variability – volcanoes, solar and internal variability – are expected to act in the direction of cooling during this period.
In the midst of all the angst about 1.5oC or 2.0oC warming or more, as defined relative to some mythical time when climate was alleged to be ‘stable’ and (relatively) uninfluenced by humans, we lose sight of the fact that we have a better baseline period – now. One advantage of using ‘now’ as a baseline for future climate change is that we have good observations to describe the climate of ‘now’.
While most of the focus of climate projections is on 2100, the period circa 2020-2050 is of particular importance for several reasons:
It is the period for meeting UNFCCC targets for emissions reductions Many financial and infrastructure decisions will be made on this time scale The actual evolution of the climate over this period will influence 1) and 2) above; ‘surprises’ could have adverse impacts on decisions related to 1) and 2).
Global climate/earth system models have little skill on decadal time scales. To address this issue, CMIP5 and CMIP6 are conducting initialized, decadal scale simulations out to 35 years. While I haven’t seen any CMIP6 decadal results yet, I do follow this literature. Punchline is that there is some skill in simulating the Atlantic Multidecadal Oscillation (AMO) out to 8-10 years, but otherwise not much overall skill.
I have previously criticized the interpretation CMIP5 simulations as actual climate change scenarios – instead, these simulations show the sensitivity of climate to different emissions scenarios. They neglect scenarios of future solar variability, volcanic eruptions, and the correct phasing and amplitude of multidecadal variability associated with ocean circulations. The argument for dismissing these factors is that they are smaller than emissions forcing. Well, cumulatively and on decadal to multi-decadal timescales, this is not necessarily true.
And in the CMIP6 era, we now have sufficient information and understanding so that we can generate plausible scenarios of volcanic and solar forcing for the 21st century, as well as for the AMO.
I have developed a semi-empirical approach to formulating 21st century climate change scenarios that rely only indirectly on climate models. Multiple scenarios are generated for each driver of the forecast (natural and anthropogenic), with an emphasis on plausible scenarios (rather than extreme scenarios that cannot completely be ruled out).
Note: in what follows, many references are cited. I don’t have time now to pull together a full bibliography, but I have provided hyperlinks to the key references.
Manmade global warming
The approach used here is to use as much as possible the new information becoming available for CMIP6: new emission scenarios, new considerations regarding climate model sensitivity to CO 2 .
Similar to the recent IPCC SR1.5 Report, no attempt is made to use CMIP6 Earth System Model outputs. Following the IPCC SR1.5, scenarios of global warming are driven by scenarios of cumulative emissions. The individual cumulative emission scenarios between 2020 and 2050 are then translated into a global temperature increase using a range of values of the Transient Climate Response to Cumulative Carbon Emissions (TCRE). This approach is illustrated in the following figure:
Figure 1: CO2-induced warming as a function of cumulative emissions and TCRE. Millar et al
Emissions scenarios
For the forthcoming IPCC AR6, a new set of emissions scenarios (SSP) have been issued.
The 2019 World Energy Outlook Report from the International Energy Agency (IEA) challenges the near term SSP scenario projections through 2040. They examined three scenarios: a current policy scenario (CPS) where no new climate or energy policies are enacted by countries, a stated policies scenario (STPS) where Paris Agreement commitments are met, and a sustainable development scenario (SDS) where rapid mitigation limits late 21st warming to well below 2°C. Both the IEA CPS and STPS scenarios can be considered as business-as-usual where either current policies or current commitments continue, but no additional climate policies are adopted after that point.
Figure 2 compares the IEA fossil fuel emissions projections to scenarios being used in the IPCC AR6. The figure indicates that the IEA CPS emissions are between the SSP2- RCP4.5 and SSP4-RCP6.0 scenarios and the IEA STPS scenario is slightly below SSP2-RCP4.5.
Figure 2: Annual CO2 emissions from fossil fuel and industry in CPS and STPS IEA scenarios compared to the range of baseline scenarios examined in the SSP Database, as well as a subset of the baseline and mitigation scenarios chosen for use in the forthcoming IPCC AR6 report. Ritchie and Hausfather (2019) https://thebreakthrough.org/issues/energy/3c-world
In view of these considerations, I select a single scenario for consideration here: SSP2-4.5. For the timescale of this analysis (2020-2050), there is little difference between 4.5 and 6.0, and we are not currently on the 7.0 trajectory.
Cumulative emissions for SSP2-4.5 calculated from 2020 to 2050 are reported in Table 1 for both cumulative CO 2 and cumulative C (carbon). Cumulative C is used in calculating the transient climate response to cumulative carbon emissions (TCRE); note that 1000 GtC is the carbon content of 3667 GtCO 2.
Table 1: Projections of cumulative CO 2 (GtCO 2 ) and C concentrations (GtC) between 2020-2050, for 3 SSP emissions scenarios. Data from IIASA database.
For reference, the IPCC SR1.5 Report assessed that amount of additional cumulative CO 2 emissions (50th percentile) from a reference period 2006-2015 to keep additional warming to within 0.5°C is 580 GtCO 2 , and to keep additional warming to within 1.0°C is 1500 GtCO 2 .
TCRE
Translating the emissions scenarios into a global temperature increase has traditionally been conducted using global climate or earth system model simulations. However, the CMIP6 simulations using the new SSP scenarios and their analysis are currently underway. The recent IPCC SR1.5 Report chose to use values of the transient climate response to cumulative carbon emissions, or TCRE, to relate global temperature change to the cumulative emissions in the SSP scenarios.
The amount of warming the world is projected to experience from emissions is approximately linearly proportional to cumulative carbon emissions (for an overview, see Matthews et al. 2018). This relationship between temperatures and cumulative emissions is referred to as the transient climate response to cumulative carbon emissions, or TCRE. For a give value of TCRE, we can calculate the amount of warming expected over a future period in response to scenarios of cumulative carbon emission.
The IPCC AR5 provided a likely range for TCRE of 0.8°C to 2.5°C. Matthews et al. (2018) state that the current generation of full-complexity Earth-system models exhibits a range of TCRE values of between 0.8 and 2.4°C, with a median value of 1.6°C. An observationally-constrained TCRE estimate gave a 5%–95% confidence range of 0.7 −2.0°C, with a best-estimate of 1.35 ◦C (Gillett et al 2013). A more recent observationally-constrained estimate is provided by Lewis (2018), who determined a best estimate of 1.05°C.
In view of these assessments, I select the following values of TCRE for scenarios: 1.0, 1.35, 1.65, 2.0°C as constituting a range of plausible values.
Table 2 provides calculations of the amount of warming between 2020 and 2050, based on SSP2-4.5 and four values of TCRE. As expected from the range of TCRE values used here, there is a factor-of-two range in the amount of emissions-driven warming expected for the period 2020-2050.
Table 2: Warming scenarios (oC) for 2050 from a 2020 baseline based on the SSP2-4.5 cumulative emissions scenario (GtC) and four values of TCRE (oC)
Projections of natural climate variability
Scenarios of future variations/changes are presented for 2030-2050 for the following:
Solar variations
Volcanic eruptions
Decadal-scale ocean circulation variability
Solar variations
With regards to solar scenarios for the 21st century, there are two issues:
How much total solar insolation (TSI) will change
How much warming, given a specific TSI.
According to the IPCC AR5, the influence of the Sun on our climate since pre-industrial times, in terms of radiative forcing, is very small compared to the variation of radiative forcing due to added anthropogenic greenhouse gases: 0.05 W/m2 vs. 2.29 W/m2. Thus, the IPCC AR5 message is that changes in solar activity are nearly negligible compared to anthropogenic forcing.
This interpretation has been challenged:
There is substantial disagreement on trends in solar activity, even in the satellite era. Several papers in the last decade have claimed that solar activity in the second part of the 20 th century was higher than any time in the past 10,000 years. Some studies claim that the Sun could have contributed at least ∼ 50% of the post 1850 global warming.
century was higher than any time in the past 10,000 years. Some studies claim that the Sun could have contributed at least ∼ 50% of the post 1850 global warming. The IPCC AR5 considered only the direct solar effects on global temperatures. It has been found that over the eleven- year solar cycle the energy that enters the Earth’s system is of the order of 1.0–1.5 W/m 2 . This is almost an order of magnitude larger than what would be expected from solar irradiance alone, and suggests that solar activity is getting amplified atmospheric processes. Candidate processes include: solar ultraviolet changes; energetic particle precipitation; atmospheric-electric-field effect on cloud cover; cloud changes produced by solar-modulated galactic cosmic rays; large relative changes in its magnetic field; strength of the solar wind.
. This is almost an order of magnitude larger than what would be expected from solar irradiance alone, and suggests that solar activity is getting amplified atmospheric processes. Candidate processes include: solar ultraviolet changes; energetic particle precipitation; atmospheric-electric-field effect on cloud cover; cloud changes produced by solar-modulated galactic cosmic rays; large relative changes in its magnetic field; strength of the solar wind. The relations between solar variations and Earth climate are many and complicated. Most of them work locally and regionally, and many are non-linear. Strong solar influences have been seen in the Pacific and Indian Oceans and also in the Arctic, among other regions.
As summarized by Svensmark (2019), satellite data demonstrate that TSI varies by as much as 0.05–0.07% over a solar cycle. At the top of the atmosphere this variation amounts to around 1 W/m2 out of a solar constant of around 1361 W/m2. At the surface, this is only 0.2 W/m2, after taking geometry and albedo into account. Model simulations and observations have shown a response of global surface temperature to TSI variations over the 11-year solar cycle of about 0.1oC (Matthes et al. 2017).
The current solar cycle 24 is the smallest sunspot cycle in 100 years and the third in a trend of diminishing sunspot cycles. Is the Sun is currently moving into a new grand minimum or just a period of low solar activity? Many solar physicists expect the sun to move into a new minimum during the 21st century: a century-level low, although several predict a minimum comparable with the Dalton or even the Maunder Minimum.
In CMIP5, climate projections were based on a stationary-Sun scenario, obtained by simply repeating solar cycle 23, which ran from April 1996 to June 2008, which is the third strongest solar cycle since 1850. Clearly, such a stationary scenario is not representative of true solar activity, which exhibits cycle-to-cycle variations and trends. Therefore, in CMIP6 more realistic scenarios were developed for future solar activity, exhibiting variability at all timescales (Matthes et al. 2017). Matthes et al. present the following two scenarios (Figure 3): a reference scenario and a Maunder minimum scenario for the second half of the 21st century.
Figure 3: CMIP6 scenarios for solar forcing (TSI): reference scenario (top); Maunder minimum scenario (bottom). Matthes et al. (2017)
If a Maunder minimum-scale event were to occur in the 21st century, how much cooling would this cause? As summarized by Svensmark (2019), a majority of reconstructions find only small changes in overall secular solar radiative output: since the Maunder Minimum, TSI is believed to have increased by around 1 W/m2, which corresponds to 0.18 W/m2 at the Earth’s surface – this is the same magnitude of the amplitude of the 11 year solar cycle. Jones et al. (2012) used a simple climate model to estimate that the likely reduction in the warming by 2100 from a ‘Maunder minimum’ scale event to be between 0.06 and 0.1 °C. Fuelner and Rahmstorf (2010) estimated that another solar minimum equivalent to the Dalton and Maunder minima would cause 0.09°C and 0.26°C cooling, respectively. Meehl et al. (2013) estimated a Maunder minimum cooling of 0.3°C.
These calculations ignored any indirect solar effects, which would arguably increase these numbers by up to a factor of 3 to 7. Shaviv (2008) used the oceans as a calorimeter to measure the radiative forcing variations associated with the solar cycle. Shaviv found that the energy that enters the oceans over a solar cycle is 5–7 times larger than the 0.1% change in TSI, thus implying the necessary existence of an amplification mechanism. Scafetta (2013) showed that the large climatic variability observed since the medieval times can be correctly interpreted only if the climatic effects of solar variability on the climate have been severely underestimated by the climate models by a 3 to 6 factor. Svensmark (2019) made a comparable argument using borehole temperatures for the period since the Medieval Warm Period, finding an amplification of a factor of 5 to 7 over the warming expected from a drop in TSI. If an amplification factor is included of these magnitudes, then a surface temperature decrease of up to 1oC (or even more) from a Maunder minimum could be expected.
Three scenarios for solar variability are used here:
No variability (CMIP5)
CMIP6 reference scenario, with factor of two amplification by solar indirect effects
CMIP6 Maunder Minimum scenario, with factor of four amplification by solar indirect effects (note: the period 2020-2050 has lower values of TSI than the reference scenario, but the actual Minimum is in the latter half of the 21st century).
Note: the CMIP6 values of changes in TSI are ‘eyeballed’ from Figure 3 (I did not download the CMIP6 solar projections). I would greatly appreciate other interpretations of the values of surface cooling to infer from the CMIP6 solar scenarios.
Table 3. Scenarios of solar cooling (oC), relative to the CMIP5 solar cycle
Volcanoes
The 21st century CMIP5 climate model simulations did not include any radiative forcing from future volcanic eruptions. While volcanic eruptions are not predictable, a scenario of zero radiative forcing from volcanoes in the 21st century is a poor assumption. Further, assuming a repeat of the 20th century volcanic radiative forcing is not a very good assumption, either.
In the past decade, there have been two major paleoclimate reconstructions of volcanic eruptions in the recent millennia. Gao et al. (2008) examined ice core records and their following reconstruction for sulfate ejection from volcanic eruptions. A more recent reconstruction by Sigl et al. (2015) is provided below, presented in terms of global volcanic aerosol radiative forcing. These reconstructions put into perspective the relative low level of volcanic activity since the mid 19th century.
Figure 4: Reconstruction of global volcanic aerosol radiative forcing for the past 2500 years. Sigl et al. (2015)
Because volcanic eruptions are unpredictable events, they have generally been excluded from twenty-first century climate projection protocols. Most recent projections either specify future volcanic forcing as zero or a constant background value. Bethke et al. (2017) explored how sixty possible volcanic futures, consistent with ice-core records, impact climate variability projections of the Norwegian Earth System Model (NorESM; ECS=3.2C) under RCP4.5. Clustered occurrence of strong tropical eruptions has contributed to sustained cold periods such as the Little Ice Age, where the longer- term climate impacts are mediated through ocean heat content anomalies and ocean circulation changes. Extreme volcanic activity can potentially cause extended anomalously cold periods.
Figure 5: Annual-mean GMST. Ensemble mean (solid) of VOLC (stochastic volcanic forcing; blue), VOLC-CONST (average 1850-2000 volcanic forcing; magenta) and NO-VOLC (red/orange) with 5–95% range (shading) and ensemble minima/maxima (dots) for VOLC and NO-VOLC; evolution of the most extreme member (black). Bethke et al. (2017).
Based on the results of Bethke et al. (2017), three volcanic scenarios for cooling are used, related to the decadal values shown in Figure 5:
No forcing
50 th percentile value: mean forcing
percentile value: mean forcing 95th percentile value: large forcing
Figure 6. Decadal means of GMST relative to pre-industrial. Ensemble mean (solid) with 5–95% range (shading) of VOLC (blue) and NO-VOLC (red).
Table 4 shows the decadal scenarios of volcanic cooling, consistent with Figure 6.
Table 4. Decadal scenarios of volcanic cooling (oC). From Bethke et al. (2017)
Internal variability
Variations in global mean surface temperature are also associated with recurrent multi-decadal internal variability associated with large-scale ocean circulations. However, separating the internal variability from forced variability is not always straightforward owing to uncertainties in external forcing.
The multi-decadal internal variability (50-80 year band) has been estimated to have a peak-to-peak amplitude of global surface temperature as high as 0.3-0.4oC (Tung and Zhou, 2012), accounting for about half of the late 20th century warming. DelSole et al. (2010) estimated a peak-to-peak global temperature change of 0.24oC from internal variability. By contrast, Stolpe (2016) estimated a maximum peak-to-peak amplitude of 0.16oC. Knutson et al. (2016) used the GFDL CM3 model, which has strong internal multidecadal variability, to identify several periods that exceed 0.5oC for global mean surface temperature, indicating that data records of ~160 years are too short for a full sampling of multi-decadal internal climate variability.
Most analyses have identified Atlantic Multidecadal Variability as having the dominant imprint on global and Northern Hemisphere temperatures. Identification of ENSO as a driver of global mean temperature variations or response signal remains contentious, with conflicting results. Bhaskar et al. (2017) characterizes ENSO as a secondary driver of variations in global mean temperature, accounting for 12% of variability over the last century, with ENSO and global mean surface temperature mutually driving each other at varied time lags.
Not taking multi-decadal variability into account in predictions of future warming under various forcing scenarios may run the risk of over-estimating the warming for the next two to three decades, when the Atlantic Multi-decadal Oscillation (AMO) is likely to shift into its cold phase.
Analysis of historical and paleoclimatic records suggest that a transition to the cold phase of the AMO is expected prior to 2050. Enfield and Cid-Serrano (2006) used paleoclimate reconstructions of the AMO to develop a probabilistic projection of the next AMO shift. Figure 7 shows the probability of an AMO shift relative to the number of years since the last regime shift. The previous regime shift occurred in 1995; hence in 2020, it has been 23 years since the previous shift. Figure 7 indicates that a shift to the cold phase is expected to occur within the next 15 years, with a 50% probability of the shift occurring in the next 6 years.
Figure 7. Probability of an AMO regime shift relative the number of years since the last regime shift. Source: Enfield and Cid-Serrano (2006)
The timing of a shift to the AMO cold phase is not predictable; it depends to some extent on unpredictable weather variability (Johnstone, 2020). Johnstone’s analysis shows that low-frequency changes in North Atlantic SSTs since 1880 are objectively identified as a series of alternating ‘regime shifts’ with abrupt (~1-year) transitions dated to 1902, 1926, 1971 and 1995 (Figure 8). In the recent historical record (back to 1880), these sharp changes punctuate longer quasi-stable periods of 24 years (1902-1925), 45 years (1926-1970), and 24 years (1971-1994), while the latest, and warmest regime on record has persisted with little net change from 1995 through 2019 (25 years). Previous cool shifts in 1902 and 1971 shared similar -0.2°C amplitudes, following extended periods of relative warmth (1880-1901), (1926-1970). A negative (cool) shift within a shorter time frame (~5 years) might be tentatively inferred from a steep 2015 SST decline in the subpolar North Atlantic, behavior that might presage broader North Atlantic cooling based on early subpolar appearance of the most recent cool shift of 1971.
Figure 8. Annual SST anomalies in the subpolar (blue) and tropical (red) North Atlantic. A sharp subpolar cooling is evident in 2015. Johnstone (2020)
Guided by the above analyses, three scenarios for global temperature change associated with the AMO are presented in Table 5.
Table 5. Decadal scenarios of temperature change from internal variability (oC), associated with a transition to the cool phase of the Atlantic Multidecadal Oscillation.
Integral scenarios of temperature change: 2050
The final integral temperature change is the sum of temperature changes driven by
Emissions (4 scenario)
Volcanoes (3 scenarios)
Solar (3 scenarios)
AMO (3 scenarios)
There are 108 possible different combinations of these scenarios. Table 6 shows extreme high and low warming scenario, plus the scenario using all of the mid range values.
Table 6. Integral scenarios of global mean surface temperature change for 2020-2050.
All of the components of natural variability point to cooling during the period 2020-2050. Individually these terms are not expected to be large in the moderate scenarios. However, when summed their magnitude approaches the magnitude of the warming associated with the moderate values of TCRE – 1.35 and 1.65 oC. If the natural cooling exceeds the expected value, or TCRE is at the low end (1.0 to 1.35oC), then there could be net cooling.
The possibility and probability of 21st century decades being characterized by net cooling has been addressed by several papers. This depends on model value of ECS, and the magnitude of the predicted natural variability. Knutson et al. (2016) used the GFDL climate model (relatively high value of ECS; high internal variability) to determine that probability of global temperature trend <0 for period of 20-30 years is 2%. Bethke et al. (2017) used NorESM (ECS=3.2C) with RCP4.5. This paper examined the combination of scenarios of internal variability and volcanic eruptions. They found that occurrences of decades with negative GMST trend become more frequent if accounting for volcanic forcing, with the probability increasing from 10% in NO-VOLC to more than 16% in VOLC. The probability of decades with negative GMST trend more than doubles from 4% to 10% if the analysis is limited to the first half of the century—before the stabilization period of RCP4.5. Volcanic-induced cooling becomes increasingly important in facilitating neutral or negative temperature trends on longer timescales, in conjunction with natural internal variability effects.
In summary, decade(s) during the period 2020-2050 with zero warming or even cooling should not be particularly surprising.
Conclusions
Three main conclusions:
We are starting to narrow the uncertainty in the amount of warming from emissions that we can expect out to 2050
All three modes of natural variability – solar, volcanoes, internal variability – are expected to trend cool over the next 3 decades
Depending on the relative magnitudes of emissions driven warming versus natural variability, decades with no warming or even cooling are more or less plausible.
If you prefer your scenarios on the high side, you can include scenarios with RCP7.0 and TCRE=2.4oC, but these values don’t change the fundamental narrative presented here. You can also add 1.2oC to the values in Table 6, to make the numbers look higher. But if you want plausible scenarios, look to my Table 6, which I think bounds the range of plausible outcomes for global mean surface temperature from 2020-2050.
But what about the 2nd half of the 21st century and 2100? Uncertainties regarding emissions are much greater in the 2nd half of the 21st century. The CMIP6 solar scenarios (Reference and Maunder) show more cooling in the second half of the 21st century. Volcanic eruptions could be larger in 2nd half of 21st century (or not). After the projected cool phase of the AMO, a return to the warm phase is expected, but there is no confidence in projecting either a warm or cold phase AMO in 2100.
Apart from the ‘wild card’ of volcanic eruptions, the big uncertainty is solar indirect effects. Based on the literature survey that I’ve conducted, solar UV effects on climate seem to be at least as large as TSI effects. A factor of 2-4 (X TSI) seems completely plausible to me, and serious arguments have been presented for even higher values. I also note here that almost all estimates of ECS/TCR from observations do not include any allowances for uncertainties associated with solar indirect effects. Scafetta (2013) included solar indirect effects in an estimate of ECS, and determined an ECS value of 1.35 oC.
Neither the effects of AMO or solar indirect effects have been included in attribution analyses of warming since 1950.
So why does this analysis ‘matter’?
For those that are urgently worried about the impacts of AGW and the need to act urgently to meet deadlines related to emissions, the natural climate variability may help slow down the warming over the next few decades, allowing for time to make prudent, cost effective decisions that make sense for the long term.
Failure to anticipate and understand periods of stagnant warming or even cooling detract from the credibility of climate science and may diminish the ‘will to act.’
I look forward to your comments. I encourage you to critique and check my numbers, especially related to solar.
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climateetc--2020-02-11--Inconsistency between historical and future CMIP5 simulations

Identification of significant differences between the historical and future CMIP5 simulations for intrinsic climate sensitivities .
There are a number of climate science articles that refer to the potential for climate modelers to select from parameter variables that satisfy well the sought after direct effects for a range of inputs and thus give the modeler the option of selecting those values that also better reproduce the observed temperature changes over the historical period .
These references do not attribute the modeling influences on GMST trends to explicit tuning but rather to selecting from a range of parameter processes that can @ @ @ @ @ @ @ .
The parameter processes that are most amenable to these selections involve cloud and aerosol effects .
See for example references 1 through 10 .
In this post reference to the historical period is taken as 1861-2005 and the future period as 2006-2100 .
Numbered references in this post are assigned to papers listed in the table linked here .
My analyses have discovered significant differences between the historical and future time periods for global mean surface temperature change responses to the CMIP5 models intrinsic climate sensitivities .
For determining responses , I @ @ @ @ @ @ @ is the climate resistance as derived in Forster et al .
( 2013 ) and listed reference 11 .
This parameter comes from the relationship given in the equation ρ=κ + λ , where κ is the ocean heat uptake efficiency and λ is the feedback parameter .
The definition of climate resistance assumes the forcing to be continuously increasing , with ΔT as the change in GMST .
I assumed that the forcing ideally should be applied in equal measure for all individual models , and thus ΔF becomes a constant and @ @ @ @ @ @ @ in the temperature and forcing series , I used the trend extraction method Complete Ensemble Empirical Mode Decomposition with Adaptive Noise ( CEEMDAN ) .
See references 12 and 13 .
The original EMD on which CEEMDAN is described in N. Huang et al .
( 1998 ) and works well with data that are non stationary and non linear .
It is an empirical approach that is applied to a data set , rather than a theoretical tool that requires assumptions .
Using CEEMDAN on a time series with known noise and @ @ @ @ @ @ @ a secular trend .
To demonstrate consistency with more conventional methods , I also use in some regressions , in conjunction with the CEEMDAN method , an End Point Averaging method much like the approach used in Lewis and Curry ( 2018 ) and listed as reference 14 .
The GMST and forcing data are taken from KNMI Explorer and Meinshausen et al .
( 2011 ) , references 15 and 16 , respectively .
The ρ values are available from the literature for 25 CMIP models , e.g .
references 17 and @ @ @ @ @ @ @ the individual models from the CMIP5 4XCO2 and 1 % CO2 experiments .
For this analysis I determined that I could use the RCP 8.5 GMST and forcing data for the future period to obtain ρ values for 39 CMIP5 models .
The RCP 8.5 scenario , having a large increase in GHG forcing , a decreasing aerosol and cloud forcing and large temperature increases in response to the forcing , makes it promising ground for estimating model ρ values with little influences from natural variations and potential model-to-model forcing variations leftover from @ @ @ @ @ @ @ primarily of doing OLS regressions on the changes in GMST ( ΔT ) versus 1/ρ and with derived values called calculated ΔT and difference ΔT .
The calculated ΔT is the expected ΔT given the forcing ( ideal ) and ρ value of an individual climate model .
The difference ΔT is the difference between the calculated ΔT and the derived ΔT , where derived means the CEEMDAN or End Point Averaging derived trend from the KNMI GMST temperature data .
The regression r-squared values are reported with and without the outliers .
@ @ @ @ @ @ @ Distance of 4 times the mean distance ( reference 19 ) .
It should be of little surprise that not all of the models are going to fit closely in the regressions , given the large range of responses these models are known to have and the probabilities that model results are affected to a lesser or greater degree by modelers ’ choices of parameters and parameter values .
The point of removing outliers was to show the regression fit for almost all of the models and thus no effort was made to @ @ @ @ @ @ @ this analysis are summarized in the four figures and table below .
Figure 1 shows a regression plot of the individual model p values derived from RCP 8.5 ΔT and forcing data versus the published counterparts .
The fit is very good .
Figures 2 and 3 show the very good regression fits of the RCP 4.5 and RCP 6.0 individual model ΔT values from the 2006-2100 period versus the RCP 8.5 generated ρ values .
These regressions have the expected positive slope that ideally should result from the same forcing applied to @ @ @ @ @ @ @ GMST responding linearly to 1/ρ .
In contrast to the fits and slopes for the future period , Figure 4 for RCP 4.5 in the 1861-2005 period has a negative slope and a poor fit .
The table of regressions lists the results of twenty OLS regressions for ΔT versus 1/ρ , calculated versus derived ΔT and difference ΔT ( calculated – derived ) between various scenarios and time periods .
Critical to this analysis is the stark contrast between the historical and future period ΔT response to ρ with the assumption of @ @ @ @ @ @ @ models .
The models perform in the future period as expected while the same models in the historical period , for the most part , appear to respond to different forcings .
Most revealing in this matter are the regressions in 6 , 7 and 8 , where ΔT versus 1/ρ is regressed for the historical period with the RCP 4.5 models .
The regressions in 6 that includes all 39 of the models shows a negative but not significant slope ( p < 0.05 ) .
In regression 7 with a selection @ @ @ @ @ @ @ difference ΔT values ( calculated – derived ) the slope is very significantly positive , putting those models very much in line with the models responses in the future period .
The regression of 25 of 39 models with the largest difference ΔT values has a significant slope and it is negative .
It should be noted here that the CEEMDAN and End Point Averaging methods used concurrently to obtain GMST changes gave nearly the same regression results .
That would not always be the case as tests with simulations of known series @ @ @ @ @ @ @ a range of periodically varying components will show CEEMDAN to be the superior method in extracting a known trend component over other commonly used methods , including End Point Averaging .
Where End Point Averaging is used as judiciously as it has been in Lewis and Curry papers in selecting optimum time periods CEEMDAN and End Point Averaging will yield nearly the same results .
The important result here is that in the historical period the individual model relationship of ΔT versus 1/ρ tends for a large majority of models to have a @ @ @ @ @ @ @ suggesting that those models have had a differential forcing applied , and more importantly , that the applied forcing is compensating for a higher climate sensitivity .
Further the results show that the ideal of an externally applied forcing of the same value for all models actually does not apply in the historical period and varies for most of the models .
It also shows that approximately a third of all the models in the historical period follow what would be expected in the ideal case .
A precursory view of these results @ @ @ @ @ @ @ relatively small change in a negative forcing such as that resulting from the direct and indirect effects of aerosol forcing could affect the historical period relationships of ΔT to 1/ρ more than in the future period where the positive forcing increases more over time than in the historical period .
Qualitatively that view would be correct , but a quantitative assessment would require analysis beyond what is shown here .
This analysis shows that ( a ) a portion of the models in the historical period can respond more ideally to forcing and @ @ @ @ @ @ @ the models in the future period and ( b ) that those models in the historical period that are not responding ideally have a general tendency to have forcing applied in such a direction as to counter the models climate sensitivity towards producing smaller changes in GMST .
Moderation note : As with all guest posts , please keep your comments civil and relevant .

by Kenneth Fritsch
Identification of significant differences between the historical and future CMIP5 simulations for intrinsic climate sensitivities.
Introduction
There are a number of climate science articles that refer to the potential for climate modelers to select from parameter variables that satisfy well the sought after direct effects for a range of inputs and thus give the modeler the option of selecting those values that also better reproduce the observed temperature changes over the historical period. These references do not attribute the modeling influences on GMST trends to explicit tuning but rather to selecting from a range of parameter processes that can yield in turn a range of trends. The parameter processes that are most amenable to these selections involve cloud and aerosol effects. See for example references 1 through 10.
In this post reference to the historical period is taken as 1861-2005 and the future period as 2006-2100. Numbered references in this post are assigned to papers listed in the table linked here.
https://www.dropbox.com/s/zhslpck9lqnbwxu/References_Delta_T_vs_Climate_Resistance.docx?dl=0
My analyses have discovered significant differences between the historical and future time periods for global mean surface temperature change responses to the CMIP5 models intrinsic climate sensitivities. For determining responses, I used the relationship ΔT=ΔF/ρ, where ρ is the climate resistance as derived in Forster et al. (2013) and listed reference 11. This parameter comes from the relationship given in the equation ρ=κ + λ, where κ is the ocean heat uptake efficiency and λ is the feedback parameter. The definition of climate resistance assumes the forcing to be continuously increasing, with ΔT as the change in GMST. I assumed that the forcing ideally should be applied in equal measure for all individual models, and thus ΔF becomes a constant and ΔT=k/ρ.
Methods and Resources
To identify the secular trend in the temperature and forcing series, I used the trend extraction method Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). See references 12 and 13. The original EMD on which CEEMDAN is described in N. Huang et al. (1998) and works well with data that are non stationary and non linear. It is an empirical approach that is applied to a data set, rather than a theoretical tool that requires assumptions. Using CEEMDAN on a time series with known noise and cycles results in a faithful separation of a secular trend.
To demonstrate consistency with more conventional methods, I also use in some regressions, in conjunction with the CEEMDAN method, an End Point Averaging method much like the approach used in Lewis and Curry (2018) and listed as reference 14.
The GMST and forcing data are taken from KNMI Explorer and Meinshausen et al. (2011), references 15 and 16, respectively.
The ρ values are available from the literature for 25 CMIP models, e.g. references 17 and 18. These values are determined for the individual models from the CMIP5 4XCO2 and 1% CO2 experiments. For this analysis I determined that I could use the RCP 8.5 GMST and forcing data for the future period to obtain ρ values for 39 CMIP5 models. The RCP 8.5 scenario, having a large increase in GHG forcing, a decreasing aerosol and cloud forcing and large temperature increases in response to the forcing, makes it promising ground for estimating model ρ values with little influences from natural variations and potential model-to-model forcing variations leftover from the historical period.
The analysis consists primarily of doing OLS regressions on the changes in GMST (ΔT) versus 1/ρ and with derived values called calculated ΔT and difference ΔT. The calculated ΔT is the expected ΔT given the forcing (ideal) and ρ value of an individual climate model. The difference ΔT is the difference between the calculated ΔT and the derived ΔT, where derived means the CEEMDAN or End Point Averaging derived trend from the KNMI GMST temperature data. The regression r-squared values are reported with and without the outliers. Outliers were determined using Cook’s Distance of 4 times the mean distance (reference 19). It should be of little surprise that not all of the models are going to fit closely in the regressions, given the large range of responses these models are known to have and the probabilities that model results are affected to a lesser or greater degree by modelers’ choices of parameters and parameter values. The point of removing outliers was to show the regression fit for almost all of the models and thus no effort was made to explain the outliers.
Results
The results of this analysis are summarized in the four figures and table below.
Figure 1 shows a regression plot of the individual model p values derived from RCP 8.5 ΔT and forcing data versus the published counterparts. The fit is very good. Figures 2 and 3 show the very good regression fits of the RCP 4.5 and RCP 6.0 individual model ΔT values from the 2006-2100 period versus the RCP 8.5 generated ρ values. These regressions have the expected positive slope that ideally should result from the same forcing applied to all the models and the change in GMST responding linearly to 1/ρ. In contrast to the fits and slopes for the future period, Figure 4 for RCP 4.5 in the 1861-2005 period has a negative slope and a poor fit.
The table of regressions lists the results of twenty OLS regressions for ΔT versus 1/ρ, calculated versus derived ΔT and difference ΔT (calculated – derived) between various scenarios and time periods. Critical to this analysis is the stark contrast between the historical and future period ΔT response to ρ with the assumption of a single forcing value for the individual models. The models perform in the future period as expected while the same models in the historical period, for the most part, appear to respond to different forcings. Most revealing in this matter are the regressions in 6, 7 and 8, where ΔT versus 1/ρ is regressed for the historical period with the RCP 4.5 models. The regressions in 6 that includes all 39 of the models shows a negative but not significant slope (p <0.05). In regression 7 with a selection of the 14 models with the smallest difference ΔT values (calculated – derived) the slope is very significantly positive, putting those models very much in line with the models responses in the future period. The regression of 25 of 39 models with the largest difference ΔT values has a significant slope and it is negative.
It should be noted here that the CEEMDAN and End Point Averaging methods used concurrently to obtain GMST changes gave nearly the same regression results. That would not always be the case as tests with simulations of known series compositions of white and red noise and a range of periodically varying components will show CEEMDAN to be the superior method in extracting a known trend component over other commonly used methods, including End Point Averaging. Where End Point Averaging is used as judiciously as it has been in Lewis and Curry papers in selecting optimum time periods CEEMDAN and End Point Averaging will yield nearly the same results.
Discussion and Conclusions:
The important result here is that in the historical period the individual model relationship of ΔT versus 1/ρ tends for a large majority of models to have a negative slope which in effect is strongly suggesting that those models have had a differential forcing applied, and more importantly, that the applied forcing is compensating for a higher climate sensitivity. Further the results show that the ideal of an externally applied forcing of the same value for all models actually does not apply in the historical period and varies for most of the models. It also shows that approximately a third of all the models in the historical period follow what would be expected in the ideal case.
A precursory view of these results might lead to the conclusion that a relatively small change in a negative forcing such as that resulting from the direct and indirect effects of aerosol forcing could affect the historical period relationships of ΔT to 1/ρ more than in the future period where the positive forcing increases more over time than in the historical period. Qualitatively that view would be correct, but a quantitative assessment would require analysis beyond what is shown here.
This analysis shows that (a) a portion of the models in the historical period can respond more ideally to forcing and corresponding to the case for nearly all the models in the future period and (b) that those models in the historical period that are not responding ideally have a general tendency to have forcing applied in such a direction as to counter the models climate sensitivity towards producing smaller changes in GMST.
Moderation note: As with all guest posts, please keep your comments civil and relevant.
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climateetc--2020-02-08--Economic  impact of energy consumption change caused by global warming

A new paper ‘ Economic impact of energy consumption change caused by global warming ’ finds global warming may be beneficial .
In this blog post we reproduce the Abstract , Policy Implications and Conclusions and parts of the Introduction , Results and Discussion .
We encourage you to read the entire paper .
Abstract : This paper tests the validity of the FUND model ’ s energy impact functions , and the hypothesis that global warming of 2 °C or more above pre-industrial times would negatively impact the global economy .
Empirical data of energy expenditure and average temperatures @ @ @ @ @ @ @ are compared with projections using the energy impact functions with non-temperature drivers held constant at their 2010 values .
The empirical data indicates that energy expenditure decreases as temperatures increase , suggesting that global warming , by itself , may reduce US energy expenditure and thereby have a positive impact on US economic growth .
These findings are then compared with FUND energy impact projections for the world at 3 °C of global warming from 2000 .
The comparisons suggest that warming , by itself , may reduce global energy consumption .
If @ @ @ @ @ @ @ FUND projections for the non-energy impact sectors are valid , 3 °C of global warming from 2000 would increase global economic growth .
In this case , the hypothesis is false and policies to reduce global warming are detrimental to the global economy .
We recommend the FUND energy impact functions be modified and recalibrated against best available empirical data .
Our analysis and conclusions warrant further investigation .
There is a scientific hypothesis and political acceptance that global warming of 2 °C or more above pre-industrial times would have a negative impact @ @ @ @ @ @ @ is supported by economic models that rely on impact functions and many assumptions .
However , the data needed to calibrate the impact functions is sparse , and the uncertainties in the modelling results are large .
The negative overall impact projected by at least one of the main models , Climate Framework for Uncertainty , Negotiation and Distribution ( FUND ) , is mostly due to one impact sector – energy consumption .
However , the projected negative impact seems to be at odds with empirical data .
If this paper ’ @ @ @ @ @ @ @ data are correct , and if the impact functions for the non-energy sectors are correct , then the overall economic impact of global warming would be beneficial .
If true , the implications for climate policy are substantial .
Integrated Assessment Models ( IAM ) approximately reproduce the projections from the Global Climate Models ( GCM ) and apply impact functions to estimate the economic impacts of global warming .
The impact functions are derived from and calibrated to what the developers assess are the most suitable studies of the impacts .
The @ @ @ @ @ @ @ projections of population , gross domestic product ( GDP ) , per capita income , elasticities and technology progress in energy provision .
Various studies conclude that the impact functions ( also called damage functions ) used in the IAMs are derived from inadequate empirical data .
For instance , Pindyck says “ when it comes to the damage function , however , we know almost nothing , so developers of IAMs can do little more than make up functional forms and corresponding parameter values .
And that is pretty much what they @ @ @ @ @ @ @ al.
, the IAM damage functions “ are generated from a remarkable paucity of data and are thus of low reliability ” .
The National Academies of Sciences , Engineering and Medicine ( NAS ) says FUND needs further justification for the damage functions , the adaptation assumptions for the different sectors , the regional distribution of damages , and the parametric uncertainties overall .
Tol says the impact of climate change has not received sufficient attention ; he says “ there is either very little solid evidence , no conclusive evidence , @ @ @ @ @ @ @ .
NAS reviews the damage functions of the three main IAMs , discusses alternative approaches , reviews recent literature on damage estimation , and offers recommendations for a new damage module .
It says that much of the literature on which the damage functions are based is dated and , in many cases , does not reflect recent advances in the scientific literature .
For example , the FUND energy impact parameters are calibrated to reproduce the results of the 1996 papers by Downing et al .
and the income elasticity results of @ @ @ @ @ @ @ .
FUND is one of the three most cited IAMs ; Bonen et al.
, National Research Council and NAS compare them .
FUND is the most complex .
FUND disaggregates by sixteen world regions and eight main impact sectors ( agriculture , forestry , water resources , sea level rise , ecosystems , health , extreme weather , and energy consumption ) .
This enables analysts to conduct sensitivity analyses and to separately test individual impact functions .
Tol used the national version of FUND3.6 to backcast the economic impact of global @ @ @ @ @ @ @ century and projected the impacts for the 21st century .
Tol fitted the backcast results to observations of 20th century sectoral impacts .
Tol is an important study because it estimates the impacts for the most significant impact sectors , globally and by region .
It also estimates the total impact on all sectors .
The bottom panel of Figure 1 suggests that an increase of up to around 4 °C Global Mean Surface Temperature ( GMST ) above pre-industrial times would be beneficial for the total of all sectors if the projected @ @ @ @ @ @ @ Energy consumption is projected to have a substantial negative impact during the 21st century ; in fact , its negative impact exceeds the total impact of all other sectors , which is positive , from about 2080 .
The striking change in trend of the energy impact at the turn of the century inspired this study .
The trend was positive as GMST increased by 0.61 °C during the 20th century but FUND projects it will be substantially negative for the 21st century as GMST is projected to increase further .
That is @ @ @ @ @ @ @ the impacts were positive , FUND projects continued global warming would have negative impacts for the global economy .
Contrary to the FUND energy projection for the period 2000–2100 , the US Energy Information Administration ( EIA ) empirical data appear to indicate that global warming would reduce US energy expenditure and , therefore , contribute positive economic impacts for the USA .
The paper infers that the impacts of global warming on the US economy may be indicative of the impacts on the global economy .
If the economic impact of energy @ @ @ @ @ @ @ if the total of the sectoral projections in Figure 1 , other than for energy consumption , is approximately correct , global warming would be beneficial up to around 3 °C relative to 2000 , and 4 °C relative to pre-industrial times .
The significance of these findings for climate policy is substantial .
For instance , policies that aim to reduce global warming would not be economically justifiable .
Therefore , the economic impact of energy consumption projected in Tol , and by FUND3.9 , warrants investigation if FUND is to be @ @ @ @ @ @ @ the validity of the FUND energy impact functions against US empirical data .
It examines EIA data for the USA to investigate whether the impact of global warming on US energy consumption would reduce or increase US economic growth and compares the results with the energy projections .
Next it investigates the projections for FUND ’ s 16 world regions .
Lastly , it discusses some policy implications .
Figure 9 compares the projected US energy expenditure impacts against the impacts calculated from the EIA empirical data .
Figure 9 : Economic impact @ @ @ @ @ @ @ GMST change , relative to 2000 .
Pink solid line is the Julia FUND3.9 projection .
Pink dashed line is the projection with non-temperature drivers constant at 2010 values .
The orange dashed line is from the EIA data .
Figure 9 shows the projected impacts are substantially negative whereas the EIA data shows they are positive .
Figure 15 plots the global economic impacts by sector as a function of GMST change from 2000 to 2100 projected by FUND3.9 with non-temperature drivers included .
The total of all impact sectors , and @ @ @ @ @ @ @ shown .
Figure 15 : FUND3.9 projected global sectoral economic impact of climate change as a function of GMST change from 2000 .
Total* is of all impact sectors except energy .
With energy impacts excluded , FUND projects the global impacts to be +0.2 % of GDP at 3 °C GMST increase from year 2000 .
With the energy impact functions misspecifications corrected , and all other impacts are as projected , the projected total economic impact may be more positive .
The conclusion that 3 °C of global warming may be @ @ @ @ @ @ @ in part , on the total of the non-energy impact projections being correct , or more positive .
Whether this is the case needs to be tested .
The economic impact of climate policies is likely to be substantial .
It is the sum of the economic impact of the policies and the cost of implementing and maintaining the policies .
If global warming is beneficial , as this study indicates may be the case , then the total economic impact is the sum of the forgone benefits of the avoided global warming @ @ @ @ @ @ @ warming .
Our analysis suggests that the overall impact of global warming may be positive – that is , it would increase global economic growth .
If this is correct , then the positive impacts can be maximised and the negative impacts minimised by increasing wealth , but not by reducing global warming .
Tol concludes that the negative impacts of global warming can be reduced by reducing global warming and/or reducing poverty .
However , if global warming is beneficial , then polices aimed at reducing global warming are reducing global economic @ @ @ @ @ @ @ in temperature resulting from the Paris Agreement promises would be minimal but at high cost .
For example , Lomborg says that all Paris promises 2016–2030 will reduce global temperatures by just 0.05 °C in 2100 , and by 0.17 °C if they continue to 2100 .
He estimates the most likely cost would be $ 1,848 billion per year in 2030 .
This is about 2 % of projected world GDP in 2030 , and this estimate does not include all costs of the climate change industry .
Other studies also indicate @ @ @ @ @ @ @ global warming is high .
For example , Climate Change Business Journal estimates put the climate change industry in 2013 at $ 1,405 billion , about 1.9 % of world GDP .
Further , Insurance Journal says that the ‘ climate change industry ’ grew at 17–24 % annually 2005–2008 , 4–6 % following the recession , and 15 % in 2011 .
These growth rates are much higher than the growth rate of the world economy implying that , if they continue , which is likely with international protocols , accords and @ @ @ @ @ @ @ Paris , the cost of climate policies will continue to escalate .
This study tests the validity of the FUND energy impact functions by comparing the projections against empirical space heating and space cooling energy data and temperature data for the USA .
Non-temperature drivers are held constant at their 2010 values for comparison with the empirical data .
The impact functions are tested at 0° to 3 °C of global warming from 2000 .
The analysis finds that , contrary to the FUND projections , global warming of 3 °C relative to @ @ @ @ @ @ @ , therefore , would have a positive impact on US economic growth .
FUND projects the economic impact to be -0.80 % of GDP , whereas our analysis of the EIA data indicates the impact would be +0.07 % of GDP .
We infer that the impact of global warming on energy consumption may be positive for the regions that produced 82 % of the world ’ s GDP in 2010 and , by inference , may be positive for the global economy .
The significance of these findings for climate policy is @ @ @ @ @ @ @ impact projections , other than energy , are correct , and the projected economic impact of energy should actually be near zero or positive rather than negative , then global warming of up to around 3 °C relative to 2000 , and 4 °C relative to pre-industrial times , would be economically beneficial , not detrimental .
In this case , the hypothesis that global warming would be harmful to the global economy this century may be false , and policies to reduce global warming may not be justified .
Not adopting policies @ @ @ @ @ @ @ economic benefits of warming and avoid the economic costs of those policies .
The discrepancy between the impacts projected by FUND and those found from the EIA data may be due to a substantial proportion of the impacts ( 37 % for the US and 67 % for the world ) being due to non-temperature drivers , not temperature change , and to some incorrect energy impact function parameter values .
We recommend that the FUND energy impact functions be modified and recalibrated against best available empirical data .
Further , we recommend @ @ @ @ @ @ @ functions be tested .

by Peter Lang and Ken Gregory
A new paper ‘Economic impact of energy consumption change caused by global warming’ finds global warming may be beneficial.
In this blog post we reproduce the Abstract, Policy Implications and Conclusions and parts of the Introduction, Results and Discussion. We encourage you to read the entire paper.
Abstract: This paper tests the validity of the FUND model’s energy impact functions, and the hypothesis that global warming of 2 °C or more above pre-industrial times would negatively impact the global economy. Empirical data of energy expenditure and average temperatures of the US states and census divisions are compared with projections using the energy impact functions with non-temperature drivers held constant at their 2010 values. The empirical data indicates that energy expenditure decreases as temperatures increase, suggesting that global warming, by itself, may reduce US energy expenditure and thereby have a positive impact on US economic growth. These findings are then compared with FUND energy impact projections for the world at 3 °C of global warming from 2000. The comparisons suggest that warming, by itself, may reduce global energy consumption. If these findings are correct, and if FUND projections for the non-energy impact sectors are valid, 3 °C of global warming from 2000 would increase global economic growth. In this case, the hypothesis is false and policies to reduce global warming are detrimental to the global economy. We recommend the FUND energy impact functions be modified and recalibrated against best available empirical data. Our analysis and conclusions warrant further investigation.
Introduction
There is a scientific hypothesis and political acceptance that global warming of 2 °C or more above pre-industrial times would have a negative impact on global economic growth. This hypothesis is supported by economic models that rely on impact functions and many assumptions. However, the data needed to calibrate the impact functions is sparse, and the uncertainties in the modelling results are large. The negative overall impact projected by at least one of the main models, Climate Framework for Uncertainty, Negotiation and Distribution (FUND), is mostly due to one impact sector – energy consumption. However, the projected negative impact seems to be at odds with empirical data. If this paper’s findings from the empirical energy consumption data are correct, and if the impact functions for the non-energy sectors are correct, then the overall economic impact of global warming would be beneficial. If true, the implications for climate policy are substantial.
Integrated Assessment Models (IAM) approximately reproduce the projections from the Global Climate Models (GCM) and apply impact functions to estimate the economic impacts of global warming. The impact functions are derived from and calibrated to what the developers assess are the most suitable studies of the impacts. The impact functions require many assumptions, including projections of population, gross domestic product (GDP), per capita income, elasticities and technology progress in energy provision.
Various studies conclude that the impact functions (also called damage functions) used in the IAMs are derived from inadequate empirical data. For instance, Pindyck says “when it comes to the damage function, however, we know almost nothing, so developers of IAMs can do little more than make up functional forms and corresponding parameter values. And that is pretty much what they have done.” According to Kolstad et al., the IAM damage functions “are generated from a remarkable paucity of data and are thus of low reliability”. The National Academies of Sciences, Engineering and Medicine (NAS) says FUND needs further justification for the damage functions, the adaptation assumptions for the different sectors, the regional distribution of damages, and the parametric uncertainties overall. Tol says the impact of climate change has not received sufficient attention; he says “there is either very little solid evidence, no conclusive evidence, or no quantification of welfare impacts”.
NAS reviews the damage functions of the three main IAMs, discusses alternative approaches, reviews recent literature on damage estimation, and offers recommendations for a new damage module. It says that much of the literature on which the damage functions are based is dated and, in many cases, does not reflect recent advances in the scientific literature. For example, the FUND energy impact parameters are calibrated to reproduce the results of the 1996 papers by Downing et al. and the income elasticity results of the 1995 paper by Hodgson and Miller.
FUND is one of the three most cited IAMs; Bonen et al., National Research Council and NAS compare them. FUND is the most complex. FUND disaggregates by sixteen world regions and eight main impact sectors (agriculture, forestry, water resources, sea level rise, ecosystems, health, extreme weather, and energy consumption). This enables analysts to conduct sensitivity analyses and to separately test individual impact functions.
Tol used the national version of FUND3.6 to backcast the economic impact of global warming for these sectors for the 20th century and projected the impacts for the 21st century. Tol fitted the backcast results to observations of 20th century sectoral impacts. Tol is an important study because it estimates the impacts for the most significant impact sectors, globally and by region. It also estimates the total impact on all sectors.
The bottom panel of Figure 1 suggests that an increase of up to around 4 °C Global Mean Surface Temperature (GMST) above pre-industrial times would be beneficial for the total of all sectors if the projected energy impacts for 2000–2100 are excluded. Energy consumption is projected to have a substantial negative impact during the 21st century; in fact, its negative impact exceeds the total impact of all other sectors, which is positive, from about 2080.
The striking change in trend of the energy impact at the turn of the century inspired this study. The trend was positive as GMST increased by 0.61 °C during the 20th century but FUND projects it will be substantially negative for the 21st century as GMST is projected to increase further. That is, whereas the observations for 1900–2000 show the impacts were positive, FUND projects continued global warming would have negative impacts for the global economy.
Contrary to the FUND energy projection for the period 2000–2100, the US Energy Information Administration (EIA) empirical data appear to indicate that global warming would reduce US energy expenditure and, therefore, contribute positive economic impacts for the USA. The paper infers that the impacts of global warming on the US economy may be indicative of the impacts on the global economy.
If the economic impact of energy is near zero or positive, and if the total of the sectoral projections in Figure 1, other than for energy consumption, is approximately correct, global warming would be beneficial up to around 3 °C relative to 2000, and 4 °C relative to pre-industrial times. The significance of these findings for climate policy is substantial. For instance, policies that aim to reduce global warming would not be economically justifiable. Therefore, the economic impact of energy consumption projected in Tol, and by FUND3.9, warrants investigation if FUND is to be used for policy.
This paper tests the validity of the FUND energy impact functions against US empirical data. It examines EIA data for the USA to investigate whether the impact of global warming on US energy consumption would reduce or increase US economic growth and compares the results with the energy projections. Next it investigates the projections for FUND’s 16 world regions. Lastly, it discusses some policy implications.
Results
Figure 9 compares the projected US energy expenditure impacts against the impacts calculated from the EIA empirical data.
Figure 9: Economic impact of US energy expenditure as functions of GMST change, relative to 2000. Pink solid line is the Julia FUND3.9 projection. Pink dashed line is the projection with non-temperature drivers constant at 2010 values. The orange dashed line is from the EIA data.
Figure 9 shows the projected impacts are substantially negative whereas the EIA data shows they are positive.
Discussion
Figure 15 plots the global economic impacts by sector as a function of GMST change from 2000 to 2100 projected by FUND3.9 with non-temperature drivers included. The total of all impact sectors, and the total excluding energy, are also shown.
Figure 15: FUND3.9 projected global sectoral economic impact of climate change as a function of GMST change from 2000. Total* is of all impact sectors except energy.
With energy impacts excluded, FUND projects the global impacts to be +0.2% of GDP at 3 °C GMST increase from year 2000. With the energy impact functions misspecifications corrected, and all other impacts are as projected, the projected total economic impact may be more positive.
The conclusion that 3 °C of global warming may be beneficial for the global economy depends, in part, on the total of the non-energy impact projections being correct, or more positive. Whether this is the case needs to be tested.
Policy Implications
The economic impact of climate policies is likely to be substantial. It is the sum of the economic impact of the policies and the cost of implementing and maintaining the policies. If global warming is beneficial, as this study indicates may be the case, then the total economic impact is the sum of the forgone benefits of the avoided global warming plus the cost of policies to mitigate warming.
Our analysis suggests that the overall impact of global warming may be positive – that is, it would increase global economic growth. If this is correct, then the positive impacts can be maximised and the negative impacts minimised by increasing wealth, but not by reducing global warming. Tol concludes that the negative impacts of global warming can be reduced by reducing global warming and/or reducing poverty. However, if global warming is beneficial, then polices aimed at reducing global warming are reducing global economic growth.
According to Lomborg any reductions in temperature resulting from the Paris Agreement promises would be minimal but at high cost. For example, Lomborg says that all Paris promises 2016–2030 will reduce global temperatures by just 0.05 °C in 2100, and by 0.17 °C if they continue to 2100. He estimates the most likely cost would be $1,848 billion per year in 2030. This is about 2% of projected world GDP in 2030, and this estimate does not include all costs of the climate change industry.
Other studies also indicate that the cost of policies to reduce global warming is high. For example, Climate Change Business Journal estimates put the climate change industry in 2013 at $1,405 billion, about 1.9% of world GDP. Further, Insurance Journal says that the ‘climate change industry’ grew at 17–24% annually 2005–2008, 4–6% following the recession, and 15% in 2011. These growth rates are much higher than the growth rate of the world economy implying that, if they continue, which is likely with international protocols, accords and agreements such as Kyoto, Copenhagen and Paris, the cost of climate policies will continue to escalate.
Conclusions
This study tests the validity of the FUND energy impact functions by comparing the projections against empirical space heating and space cooling energy data and temperature data for the USA. Non-temperature drivers are held constant at their 2010 values for comparison with the empirical data. The impact functions are tested at 0° to 3 °C of global warming from 2000.
The analysis finds that, contrary to the FUND projections, global warming of 3 °C relative to 2000 would reduce US energy expenditure and, therefore, would have a positive impact on US economic growth. FUND projects the economic impact to be -0.80% of GDP, whereas our analysis of the EIA data indicates the impact would be +0.07% of GDP. We infer that the impact of global warming on energy consumption may be positive for the regions that produced 82% of the world’s GDP in 2010 and, by inference, may be positive for the global economy.
The significance of these findings for climate policy is substantial. If the FUND sectoral economic impact projections, other than energy, are correct, and the projected economic impact of energy should actually be near zero or positive rather than negative, then global warming of up to around 3 °C relative to 2000, and 4 °C relative to pre-industrial times, would be economically beneficial, not detrimental.
In this case, the hypothesis that global warming would be harmful to the global economy this century may be false, and policies to reduce global warming may not be justified. Not adopting policies to reduce global warming would yield the economic benefits of warming and avoid the economic costs of those policies.
The discrepancy between the impacts projected by FUND and those found from the EIA data may be due to a substantial proportion of the impacts (37% for the US and 67% for the world) being due to non-temperature drivers, not temperature change, and to some incorrect energy impact function parameter values.
We recommend that the FUND energy impact functions be modified and recalibrated against best available empirical data. Further, we recommend that the validity of the non-energy impact functions be tested.
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