The SSP greenhouse gas concentrations and their extensions to 2500

Malte Meinshausen1,2,3, Zebedee Nicholls1,2, Jared Lewis1, Matthew J. Gidden4,5, Elisabeth Vogel1,2, Mandy Freund1,6, Urs Beyerle7, Claudia Gessner7, Alexander Nauels1,5, Nico Bauer3, Josep G. Canadell8, John S. Daniel9, Andrew John1,10, Paul 5 Krummel11, Gunnar Luderer3, Nicolai Meinshausen12, Stephen A. Montzka13, Peter Rayner2,1, Stefan Reimann14, Steven J. Smith15, Marten van den Berg16, Guus J.M. Velders17,18, Martin Vollmer14, Hsaing Jui (Ray) Wang19


6
This study first describes the methods with separate parts for the updated observational data until 2018 (Section 2.1), the emission input data from the IAM scenarios and the input preparation steps undertaken (2.2), the extensions of the emissions and concentrations beyond 2100 (2.3) the MAGICC model setup 150 (2.4), and the projections of latitudinal gradients (2.5) and seasonality (2.6). We also provide a new simplified formula to reflect the Oslo Line By Line model (OLBL) radiative forcing results (Etminan et al., 2016) in order to provide the radiative forcing aggregation of the output (2.7) and discuss additional methodological steps (2.8). We then show the results and compare these to other recent observational datasets (Section 3 "Results"). A discussion section follows (Section 4 "Discussion"), which includes a 155 closer look at the two most dominant GHG forcers CO2 and CH4 and their correlation (4.1), a discussion on the most recent GHG concentration developments (4.2) and the comparison with RCPs concentrations (4.4) as well as temperatures and sea level rise projections (Error! Reference source not found.). We describe the limitations of the dataset (5), which includes issues like the integration of observational and modelled future data, missing uncertainty estimates, potential biases in future seasonality and latitudinal 160 gradients, and a lack of reference scenarios for Montreal-controlled substances. Section 6 concludes.

Methods
As for the historical concentrations, we provide 43 greenhouse gases future concentration projections, HFC-43-10mee), NF3, SF6, SO2F2, and 9 PFCs (CF4, C2F6, C3F8, C4F10, C5F12, C6F14, C7F16, C8F18, and c-C4F8). Our projections refer to atmospheric dry air mole fractions as does the historical data presented in Meinshausen et al. (2017), even though the projections are sometimes loosely referred to as 170 'concentrations'. For CO2, the usual unit is parts per million (ppm), for CH4 and N2O, the usual unit is parts per billion (ppb) and other gases are usually denoted in parts per trillion (ppt).

Updated observational data
The historical concentrations (until the end of 2014) were derived from various observational datasets of greenhouse gas concentrations, or literature studies in the case of some of greenhouse gases with lower 175 concentrations. The observational data was binned by latitudinal and longitudinal boxes, averaged for monthly values and complemented by interpolations. The historical timeseries for every greenhouse gas were separated into three elements as part of the spatio-temporal binning: i) latitudinal gradient, ii) seasonality pattern and iii) global mean. This separation then permitted the use of longer observational timeseries, such as the high latitudinal CH4 firn data -implicitly correcting for the high latitude 180 differences to the global mean that one would expect. Interpolations, regressed latitudinal gradients and seasonality patterns were employed to derive the historical dataset, but no gas cycle models.
With additional observational data being available for 2015, 2016 and 2017, the previously used observational datasources from the AGAGE and NOAA networks (Dlugokencky, 2015a, b;Prinn et al., 2018), including multiple NOAA/ESRL/GMD flask measurements, were updated and used to determine 185 the initial years of the future concentration timeseries. The result of this is that -depending on the gases -the same concentrations are used across all nine SSPs in the initial years (Table 1). As outlined below, we employed MAGICC7.0 and its calibrated gas cycles to produce concentration time series from SSP emissions beyond the observationally based period.

Emission data and their harmonisation 190
For the emission driven MAGICC7 runs that produce the future global-mean greenhouse gas timeseries, we use the SSP emission data for CO2, CH4 and N2O, HFCs, PFCs and SF6 which is available from the SSP database at IIASA (https://tntcat.iiasa.ac.at/SspDb). This emission data has already been subject to several categorisation and harmonisation steps to obtain regionally consistent (in case of CO2 and CH4) and sectorally resolved data (for more details, see Gidden  and N2O in addition to black carbon (BC), carbon monoxide (CO), ammonium (NH3), non-CH4 volatile organic compounds (NMVOC), nitrates (NOx), organic carbon (OC) and sulphate aerosol (SOx). For those 10 species, we also distinguished between fossil & industrial sources and land-use related sources. 200 8 Regional landuse CO2 emissions are not provided in the SSP database (Gidden et al., 2019), so we downscaled to the RCP regions based on historical regional emission shares in the year 2015. Given landuse CO2 emissions can be negative in some SSP scenarios, a simple scaling approach in the regional harmonisation would yield unrealistic results (i.e. regions with low or negative current net landuse emissions, like the OECD, would end up with positive emissions and the other world regions would be 205 strongly negative in the future. Instead, we applied a normalisation that assumes a negative 1.5 GtC base level against which historical regional emission shares are continued into the future, scaled with global emissions. Mathematically, the constant regional scaling factor is hence applied to the offset emission level, so that the future regional emissions ! ! (#) in year y are: which is why we assume constant regional and sectoral emission shares. This assumption does not have a bearing on final global concentrations.
For the emissions of fluorinated gases, that are listed in the Kyoto Protocol and considered here (PFCs, HFCs and SF6), namely C2F6, CF4, HFC-125, HFC-134a, HFC-143a, HFC-227ea, HFC-23, HFC-245fa, 230 HFC-43-10mee, and SF6, MAGICC7 takes the global and aggregated SSP emissions of the gas baskets as inputs, as provided by the IAM modellers using constant emission shares based on a future gas-specific scenario by Guus Velders (Velders et al., 2015) and described in Gidden et al. (2019). The basket of PFCs, HFCs and SF6 is reported in the SSP database at IIASA (https://tntcat.iiasa.ac.at/SspDb/). Some few data points were corrected in consultation with the respective IAM modelling teams, namely the 235 SSP1-1.9 emission level in 2100 for CF4 and C2F6, for which we assumed the rate of decline prolonged from the 2080 to 2090 to the 2090 to 2100 period. HFC-32 emissions were complemented from a Kigali-Agreement consistent scenario, which has also been derived from the scenarios by Velders et al. (2015). Harmonized emissions of aerosol and ozone precursor species are also available for the SSP scenarios (Hoesly et al., 2018), but not discussed in this paper. These non-GHG emissions are used here as part of the complete scenario specification needed to produce future temperature and GHG concentration 250 pathways.

Extension of emissions and concentrations beyond 2100
In 2011, the RCPs were extended beyond 2100 to provide the basis for longer-term scenario studies (Meinshausen et al., 2011b), then called 'Extended Concentration Pathways' (ECPs). Studying this 255 longer-term behaviour of the climate system is of interest for quantities that exhibit a strong long-term commitment or non-linear behaviour (e.g. sea-level rise, ice sheet dynamics). The RCP concentration extensions were -for some gases and scenarios -based on pragmatic extensions of emissions, like an RCP8.5 CO2 emission stabilisation from 2100 to 2150 with a subsequent ramp-down until 2250. For other RCPs, concentrations were held constant and the inverse CO2 emissions exhibited a near-exponential 260 decline.
Here, we present the extensions beyond 2100 of the ScenarioMIP and AerChemMIP SSPs (although we do not use a new acronym like ECPs at the time of the RCPs). The final choices differ, in some respects, from the initial sketch of these extensions that was offered in the ScenarioMIP overview paper (O'Neill et al., 2016). As described below, the collaborative exercise by the IAM modellers and MAGICC team 265 updated the original SSP extension design. In summary, the extension principles are: 1) From 2100 onwards, net negative fossil CO2 emissions are brought back to zero during the 22 nd century, while positive fossil CO2 emissions are ramped down to zero by 2250.
2) Land use CO2 emissions are brought back to zero by 2150.
3) Non-CO2 fossil greenhouse gas emissions are ramped down to zero by 2250. 270 4) Land use-related non-CO2 emissions are held constant at 2100 levels.
In the initial ScenarioMIP design (O'Neill et al., 2016), fossil CO2 emissions for SSP5-3.4-OS and SSP1-2.6 are negative at 2100 levels until 2140 and gradually increase to zero until 2190 and 2185, respectively ( Figure 2, panel a). We did not assume permanent net-negative CO2 emissions to maintain proximity to the original scenario design and in the light of biophysical and economic limits of negative emissions, as 275 well as potential side-effects (Fuss et al., 2018;Smith et al., 2016). For all scenarios with net negative fossil fuel extensions, we implemented extensions assuming constant emissions until 2140 (as suggested), but reaching zero emissions in 2190. The only exception is the SSP5-3.4-OS scenario, which was ramped back to zero by a slightly earlier date (2170) so that fossil and landuse emissions (in combination with MAGICC7.0's default setting -see section 2.4) met the design criteria of an approximate merge with 280 SSP1-2.6 concentrations in the longer-term, i.e. after 2150.
In the initial ScenarioMIP extension sketch for SSP5-8.5, total CO2 emissions were envisaged to be "less than 10 GtC/yr" by 2250 Figure 2, panel c). Having considered multiple options, we opted for a straight ramp down of fossil CO2 emissions to zero by 2250 due to its simplicity. Landuse CO2 emissions for SSP1-2.6 in the initial ScenarioMIP design were held constant at 2100 levels indefinitely. SSP5-3.4-OS 285 levels were designed to reach the same net negative landuse CO2 levels by 2120 (Figure 2, panel b).
However, the extensions presented here assume that all landuse CO2 emissions linearly phase-out between 2100 and 2150, as continuing negative landuse CO2 emissions are inconsistent with fixed 2100 landuse and land cover patterns. In the original scenario design suggestion by O'Neill et. al, all non-CO2 greenhouse gas emissions were kept constant at 2100 levels. However, the final extensions presented here 290 assume differentiated extension rules by sector. Specifically, we assumed a linear phase-out of all fossil and industrial non-CO2 emissions by 2250 (incl. aerosols etc) (see e.g. Figure 2, panel c). Similarly, synthetic industrial gases were assumed to be phased out by 2250 instead of assuming constant emissions (panel g, h). For landuse-related non-CO2 emissions, the assumption has been maintained that 2100 emission levels are held constant. That assumption seemed approximately consistent with constant 295 landuse and land cover patterns as food production activities would continue to produce certain levels of

Projecting global-mean concentrations with the MAGICC climate model
For projecting the SSP greenhouse gas concentrations, we updated several gas cycles and also used MAGICC's permafrost module, which was not switched on when projecting the RCP concentrations. The sections below describe these updates.

13
The net effect of the newly calibrated MAGICC is that Holmes    We assume that partial lifetimes related to the (changing) tropospheric OH sink scale with the OH-and temperature-dependent methane lifetime. .

Permafrost feedbacks
Earth system feedbacks from permafrost melting and its associated CO2 and CH4 releases were underrepresented in CMIP5 climate models, leading -inter alia -to an ad-hoc adjustment of remaining 390 carbon budgets by 27 GtC (100 GtCO2) in the IPCC Special Report on 1.5°C warming. Also, they were in the mineral soils, stretching from the more southerly zonal permafrost bands to the higher latitudes from now to 2200 (Figure 3 e to h).

Projecting latitudinal gradients
Compared to the previous input datasets for CMIP intercomparisons, which consisted of global-mean values only, latitudinal gradients (and seasonality) are new elements. For the historical period, these 415 latitudinal gradients and seasonally changing surface air concentrations can be estimated from the large set of in situ and flask sampling sites with monthly sampling resolution. Further back in time, when there was insufficient latitudinal coverage, the latitudinal gradient was decomposed into two empirical orthogonal functions (EOFs, the principal components). The multiplier or score (also known as the principal component time series) for the first EOF was regressed against global anthropogenic emissions. 420 Except for CO2, the score for the second EOF was kept constant. For CO2, we assumed a simplified approach by both assuming a zero intercept for the regression of global emissions versus the first EOF and a phase-out back in time of the second EOF score. These lead to the simplified and uncertain assumption that the pre-industrial CO2 gradient was zero ( extrapolate the first EOF score into the future. Given that CH4 emissions do not converge to zero in any scenario, let alone become negative, the strong North-South gradient is maintained in all scenarios.

2.6
Projecting seasonality other factors. Here, we use the net ecosystem productivity (NPP) as a proxy for future seasonality changes and regress the historically derived seasonality change EOF score with modelled future net ecosystem 450 exchange by MAGICC7. NPP in MAGICC7 is projected to increase strongly in the highest SSP5-8.5 scenario, while following a maximum-then-decline pattern in the lower SSP1-1.9 scenario. At the end of the historical period, the total seasonality is derived to have a minimum concentration deviation of -10.1 ppm in Northern mid-latitude August. Given these projected NPP changes in the high SSP5-8.5 scenario, the projected total seasonality increases to approximately twice that by 2100, a projection that comes with 455 a high degree of uncertainty.
For all other gases for which we identified a significant seasonal cycle in the historical observational data, we assume that the relative seasonality (i.e. the magnitude of monthly anomalies relative to the annual mean) stays constant, i.e. that the absolute seasonality concentration changes scale with global-mean concentrations. 460

2.7
Simplified formula to reflect radiative forcing from CO2, CH4 and N2O In order to present CO2, CH4 and N2O in our compilation of 43 greenhouse gases and their relative importance for future effective radiative forcings (ERFs), we use simplified radiative forcing formula (for radiative forcing after stratospheric temperature adjustments) that represent the Oslo line-by-line model Etminan of up to 3.6% for CO2 (see Table 1 in Etminan et al. (2016) and our Figure 4d and Table 3 below). Aside from slight model mis-fits, the original Etminan simplified formula for CO2 has a validity range of only up to 2000 ppm CO2 concentrations. Their simplified formula is an adaptation of the 470 classical approach to approximate radiative forcing by > * @A - where > is a scaling coefficient, = the CO2 concentration at time t and = $ the concentration at the reference state, normally the pre-industrial reference value. Etminan et al. introduce the overlap of the absorption spectra between CO2 and N2O and also modulate the logarithmic approximation by quadratic and linear terms. When using their suggested coefficients (a1, b1 and c1 in their Table 1), the factor > in front of the @A - / part reaches a maximum at 475 , i.e. at around 1777 ppm, when assuming = $ as the pre-industrial concentration(277.15 ppm).
For CO2 concentrations beyond 1777 ppm, the alpha value decreases, leading to an unrealistic flattening off above 2000 ppm (and eventual decline well above 3000 ppm). The highest projected SSP concentration (SSP5-8.5) reaches beyond the nominated validity range of 2000 ppm. Hence, we adapt the CO2 radiative forcing formula to assume a constant >, once > reaches its maximal value (which is around 480 1808 ppm with our optimised parameter settings -see Table 3).
In summary, building on the work of Etminan, our optimised modifications of the simplified radiative forcing expressions for CO2, CH4 and N2O as presented in Table 3 have the two advantages of (a) representing the 48 Oslo line-by-line model results within rounding errors and also (b) extending its likely validity range in line with previous forcing approximations (and pending examinations by line-by-line 485 models) to higher CO2 concentrations. However, there is one disadvantage of our simplified formula.
While our formula starts from fixed C0, N0, and M0 values at pre-industrial levels, the formulas presented in Etminan cater for the option to set C0, N0 and M0 at any value within the validity range. Hence, our formula would have to be applied twice to calculate the difference in terms of radiative forcing between a C1, N1, M1 and a C2, N2, M2 concentration state, if both are different from pre-industrial levels C0, N0 490 and M0.
We also take into account new findings regarding rapid adjustments (Smith et al., 2018). In the multimodel analysis by Smith et al. (2018), CO2 is suggested to have a slightly (~5%) higher effective radiative forcing than its instantaneous radiative forcing after stratospheric temperature adjustments alone, an adjustment also used here. While the tropospheric rapid adjustments in the case of CO2 is substantial, it 495 is largely offset by the corresponding water vapour adjustment and the cloud-related rapid adjustments

2.8
Data-flow, mean-preserving higher resolutions, and merging with historical files.
In this study's projections, the data is provided in 15° latitudinal bands with monthly resolution. These

Results
This study's projected greenhouse gas concentrations provide the 'official' greenhouse gas concentrations for the SSP scenarios. They help enable the CMIP6 exercises and span a wide range of possible futures. 515 Below, the results are presented for the various gases. The complete data repository of all projected mole fractions in various data formats, with interactive plots and factsheets is available at http://greenhousegases.science.unimelb.edu.au. The subset of the data recommended for the nine SSPs that are part of the ScenarioMIP and AerChemMIP experiments in netcdf format is also available on https://esgf-node.llnl.gov/search/input4mips/. 520 20

Carbon Dioxide
The projected CO2 concentrations range from 393 to 1135 ppm in 2100, with the low scenario SSP1-1.9 decreasing to 350 ppm by 2150 (Figure 5g). Given the assumption of zero CO2 emissions in the lower scenarios beyond that, the lower end of the projected CO2 concentrations is not projected to decrease much further. On the upper end, under the SSP5-8.5 scenario global-average concentrations are projected 525 to increase up to 2200 ppm by 2250 (Table 4 and Table 5, and see also online "GHG factsheets" at greenhousegases.science.unimelb.edu.au). The latitudinal gradient implies a difference of annual-average northern midlatitudes to South pole concentrations of about 6 ppm in current times (Figure 5b). As future seasonality is correlated with projected NPP, the CO2 seasonality change pattern (Figure 5a By approximately 2060, a zero latitudinal gradient is projected in the lowest SSP1-1.9 scenario ( Figure   5b) because CO2 emissions revert from positive to net negative. Under the highest SSP5-8.5 scenario, the northern midlatitude to South Pole difference expands to more than 23 ppm by 2100 (not shown in plot, but viewable in online data repository at greenhousegases.science.unimelb.edu.au).

Methane 540
Global-mean CH4 surface air mole fractions across the SSP scenarios are projected to range from 999.7 ppb to 3372 ppb by 2100, with maximal northern hemispheric averages being ~60 ppb higher than the global average ( Table 4). The largest difference between average Northern and Southern hemispheric concentrations (up to 120 ppb by 2100, Table 5) is in the highest CH4 emissions scenario (SSP3-7.0) and whilst the smallest difference (~70 ppb) is seen in the scenarios with the lowest global CH4 emissions 545 (SSP1-1.9, SSP1-2.6 and SSP5-3.4OS). While SSP5-8.5 is projected to be the scenario with the highest radiative forcing, because of high CO2 emissions, SSP5-8.5 is not the highest CH4 emissions scenario, 21 with both SSP3-7.0 and SSP4-6.0 suggesting higher total CH4 emission by 2100 (and in our extensions beyond 2100) (Figure 2f).

3.3
Nitrous Oxide 550 N2O concentrations are not projected to decrease at any point before 2200, regardless of the SSP scenario we consider. Even under the lowest emissions scenarios, SSP1-1.9 and SSP1-2.6, current global-average concentrations are projected to increase from 328.5 ppb in 2015 to 361 ppb by 2100 ( Table 5). Under the highest N2O scenarios (SSP3-7.0 and SSP3-7.0-lowNTCF), concentrations are projected to increase to 422 ppb by 2100 and over 500 ppb by 2500. Both seasonality and the latitudinal gradient is rather subdued 555 for N2O, as it is both a long-lived greenhouse gas and does not exhibit strong seasonal variability in either sources or sinks.

Ozone Depleting Substances and other chlorinated substances
As all ozone depleting substances' emissions are assumed to follow a single emission scenario as a result

Other fluorinated greenhouse gases
The fluorinated gas' emissions with a virtually zero ozone depleting potential -HFCs, PFCs, SF6 and NF3 -vary across the SSP scenarios. Most SSP scenarios assume strong decreases for several of these 575 substances (e.g. NF3 and SF6,), while SSP5-8.5 assumes strong increases for most of the 21 st century (Figure 2h,i). Until recently, these fluorinated gases were not controlled under the Montreal Protocol.
With the 2016 Kigali Amendment, however, a select number of HFCs have been included in the Montreal Protocol and GWP-weighted emissions of these particular HFCs will have to be phased-down globally in coming decades. When aggregating all these non-ozone depleting fluorinated gases into HFC-134a 580 equivalent concentrations, the SSP scenarios project a wide range of 2100 values, ranging from 278 ppt to more than ten-fold that value, i.e. 2985 ppt (last row in Table 4). While the HFC projections are derived from the IAM modelling team assumptions regarding the SSPs, several of the resulting HFC projections would exceed the phase-out emission levels agreed to in the Kigali Agreement.

3.6
Radiative forcing since 1750 585 In this section, we aggregate all 43 greenhouse gases' radiative forcing effect using the updated radiative forcing formula for CO2, CH4, and N2O and standard radiative efficiencies from IPCC AR5 (section 2.7).
Across the nine SSP scenarios, it is apparent that CO2 makes the largest contribution to future warming (blue parts in Figure 7), constituting between 68% and 85% of GHG radiative forcing by 2100, and 68% to 92% of radiative forcing by the time of maximum GHG-induced radiative forcing ( Table 6). In the 590 scenario with the greatest radiative forcing, SSP5-8.5, radiative forcing in 2100 is projected to be approximately 8 W/m 2 and 9.7 W/m 2 for CO2 or all GHGs, respectively (right-axis bars in Figure 7i).
This greenhouse gas induced radiative forcing is projected to increase to nearly 13 W/m 2 by 2250 under SSP5-8.5. On the lower side, the SSP1-1.9 scenario exhibits a CO2 radiative forcing of around 1 W/m 2 in 2150 and beyond, with total greenhouse gas induced forcing stabilising around 1.5 W/m 2 -equivalent to 595 CO2 concentrations of approximately 370 ppm (right axis in panel a of Figure 7).

4 Discussion
In this section, we discuss the SSP greenhouse gas concentration projections in relation to the last 2000 years of observations and cumulative carbon emissions, which are an important metric for mitigation efforts. We also provide a comparison to previous RCP pathways. 600

CO2 and CH4 concentrations
After CO2, the greenhouse gas with the second largest radiative forcing contribution in the 21 st century is CH4 (Figure 7). To a large extent, greenhouse gas induced future warming is hence influenced by the concentrations across the range of SSP scenarios. We place them in the context of the RCP scenarios as well as 475 scenarios of the IPCC Special Report on 1.5°C emissions database (https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/) (Figure 9). We focus on mid-century concentrations as they are close to the expected point of peak warming in the scenarios that are in line with the Paris 615 Agreement temperature targets of 1.5°C and well below 2.0°C. The comparison shows that SSP1-1.9 and SSP1-2.6 result in relatively similar CH4 concentrations by 2050, albeit their CO2 concentrations differ by approximately 7% (437 ppm versus 469 ppm, respectively, Table 5). The other scenario with low CH4 concentrations in 2050, i.e. SSP3-7.0-lowNTCF, falls outside the scenario space considered here, namely the SR.15 database (https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/, see Figure 9 below). This is by 620 design, as this scenario is the result of adapting a high emission scenario (SSP3-7.0) so that it features very low short-lived climate forcer emissions (Collins et al., 2017). See also Appendix C in Gidden et al.  concentrations recede over the long term to around 350ppm in case of the SSP1-1.9 scenario. Reflecting the longer lifetime and base level of agricultural emissions, N2O concentrations are not foreseen to drop below current levels in any of the investigated SSP scenarios over the coming 500 years (Figure 8).

Comparing SSP and RCP concentrations 710
For every generation of climate scenarios, whether these are the IS92, SRES, RCP or now the SSP scenarios, it is pertinent to clarify the differences and similarities of the new scenario set to the previous one(s). In particular due to the unavoidable delay in the analysis and use of the climate projections in the impact communities, clarifying the comparability to previous scenarios is paramount. Here, we compare both the greenhouse gas concentrations and an indication of global climate effects (section Error! 715 Reference source not found.).
Four RCP scenarios are now replaced in the SSP generation of scenarios with five "high priority' scenarios (4 ScenarioMIP "Tier1" cases plus SSP1-1.9) in addition to 4 additional scenarios that investigate additional forcing levels (see panels a,c in Figure 11). Aside from this difference in the sheer number of scenarios, compared to the RCPs, the actual concentration levels differ substantially for most 720 corresponding SSP scenarios. For example, the SSP5-8.5 scenario features substantially higher CO2 concentrations by 2100 and beyond than the RCP8.5 scenario (panels a,b in Figure 11). Somewhat compensating though, the CH4 concentrations by 2100 are substantially lower under the SSP5-8.5 scenario compared to the RCP8.5 scenarios (Figure 11c), and that difference is even more pronounced On the lower side of the scenarios, the most marked differences are that the new SSP1-2.6 has higher CO2 concentrations, compared to the previous RCP2.6 and SSP1-1.9 has the lowest CO2 concentrations (Figure 9 and Figure 11a). CH4 concentrations are very similar across these three scenarios by the middle 735 of the century, whereas by the end of the 21 st century, the new SSP1-1.9 and SSP1-2.6 scenarios show reduced levels of only 1000 ppb, substantially below today's CH4 concentration levels. For N2O, the story is the other way around: SSP1-1.9 and SSP1-2.6 follow almost identical concentration trajectories while the previous RCP2.6 scenario is lower.
When projecting future concentrations under the old RCP emission scenarios, the new calibration choice 740 for the gas cycles of MAGICC (section 2.4) produce increased CO2, CH4 and N2O concentrations compared to the original RCP concentration timeseries, at least for the upper scenarios (Figure 11).

Estimating the effect of latitudinally and seasonally resolved GHG concentrations on surface air temperatures in ESMs
A much-improved assimilation process results from considering seasonally and latitudinally resolved 745 GHG concentration -as individual station monthly mean measurements can easily be "bias" corrected to account for their latitudinal and seasonal variations to inform the global mean. In addition, however, the latitudinally and seasonally resolved GHG concentration data we provide also offers an opportunity to drive Earth System models with more accurate forcings, so that a comparison of the ESM historical runs with observational data can be performed -excluding ESM biases that might result from GHG 750 concentrations that are applied with a globally uniform GHG concentration levels or spatial fields that are sometimes rather dissimilar from observations ( Figures S46 and S47  higher northern latitudes -given the latitudinal gradient of methane concentrations and the seasonally higher CO2 concentrations. Indeed, we observe a regional warming signal of up to 0.4K over Northern American and Eurasian land masses, which is -in the DJF season -however latitudinally overcompensated by a strong cooling signal in the North Atlantic (Figure 12 a). In the MAM season, the slight cooling signal in the North Atlantic does not fully offset the warming over the land-masses ( Figure  765 12b), resulting in a latitudinally averaged warming signal of approximately 0.1K poleward of 65 degrees North (Figure 12d). Given the high natural variability in the higher latitudes, we consider the significance of this warming signal by comparing our warming signal to corresponding differences of arbitrarily chosen control run segments. From an approximately 4500-yearlong control run for CESM1.2.2 at preindustrial conditions, we randomly chose hundred pairs of 930-year long segments to compute the 770 variability of the differences. It turns out that our warming signals are within the min-max range of those 100 sample pairs regarding the latitudinally averaged warming differences, indicating that the expected warming signal due to applying latitudinally and seasonally resolved GHG concentration data is not beyond the min-max variability range. However, for the MAM period, there are only a few (approximately 3-5) of the paired control run differences that result in a higher warming signal compared 775 to the "lat-mon" versus "yearmean-global" differences, suggesting that the GHG warming signal is comparable in magnitude to the variability a the 5% confidence level. This is noteworthy, as many detailed processes are included theses days in ESMs at increasing computational costs that would not create a temperature signal of comparably magnitude. In the DJF period, a strong North Atlantic cooling is reducing the latitudinally aggregated warming signal. Whether that North Atlantic cooling is a result 780 of natural variability in our modestly sized 6-member ensembles or whether it is a dynamical response to generally higher latitude forcing (and possible reduced overturning in the North Atlantic thermohaline circulation branch) cannot be detected from our initial ESM runs. As one would expect, our analysis does not suggest significant latitudinal temperature perturbations at the 5% level for the JJA and SON periods (not shown), when seasonally lower CO2 concentrations are partially offset by the latitudinal gradient of 785 concentrations in the Northern hemisphere.

Limitations
In this section, we provide a number of key limitations that come with the SSP concentration datasets.
Some of these limitations arise from the underlying emission scenario data (section 5.1 and 5.2), some due to imperfect matches between recent observational and model results (section 5.3), some are intrinsic 790 30 model limitations (section 5.4 and 5.5). Likely the largest limitation is that -by design -this study provides default concentration timeseries for the future but does not represent the uncertainty range of future greenhouse gas concentrations for each scenario (section 5.6).

5.1
Limited emission variations across scenarios for gases other than CO2, CH4 and N2O.
The main focus of Integrated Assessment Models rests on projecting sectorally resolved energy, transport, 795 industry, waste, agricultural and landuse emissions for CO2, CH4 and N2O as well as air pollutant emissions. The other industrial greenhouse gases in the basket of gases of the Kyoto Protocol, namely hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), SF6 and NF3 are often modelled as a group or in subgroups. Subsequent downscaling mechanisms can then yield individual gas timeseries, although they often lack specific process dynamics, i.e. follow the same growth and decline trajectory independent of 800 their actual end-use applications. This is certainly a limitation of many of the forward-looking PFC projections.
In terms of the ozone-depleting substances (ODS), a feature, or limitation, is that the presented SSP

Individual scenario features and overall scenario spectrum
Despite all the multi-year design efforts by large research international communities, there are some inevitable limitations of the overall group of scenarios. In particular, the final set of scenarios might be 820 more appropriate for the earth system research community than for those interested in exploring policy relevant outcomes. For example, one of the scenarios that features new characteristic is the SSP5-3.4-OS scenario. That scenario assumes the greatest net negative emissions after an initial high emissions growth rate. Its high-peak-then-strong-decline feature tests the biophysical models and will be pivotal to examine the asymmetry of the ramp-up and ramp-down characteristics of the carbon cycle, ocean heat uptake and 825 multiple other Earth System properties. Yet, for policy purposes, that is substantially outside the target space of the Paris Agreement, aiming to keep temperatures to below 2°C warming.
A possible shortcoming for the climate science and impact community is that the new SSP generation of scenarios does not provide a very closely matching overlap with the RCP scenarios, as multiple scenario features are substantially different (see e.g. CO2 and CH4 concentrations in Figure 9). Thus, from a 830 climate science perspective, maintaining a single multi-gas scenario unaltered from the previous generation of scenarios could have provided a useful reference point with which to quantify the change in our climate system knowledge for future projections. Given the amount of human and material resources used for the CMIP6 runs, it is however a question of balance between historical comparability and the capability to link to earlier studies and putting resources into the most relevant, up-to-date, 835 scenarios. However, there is also a desire to use the best available forcing data to simulate the historical period. Because the actual historical evolution of concentrations and SLCF emissions has been different in detail from previous scenarios, and historical emission and concentration estimates are updated over time (e.g. Hoesly et al., 2018), the community has thus far decided to use the most up to date data for each subsequent CMIP exercise. 840

5.3
Transition issues from observational to modelled concentrations.

Main limitations due to sequential scenario generation process
The sequential and concentration-driven nature of the main ESM CMIP6 experiments poses the challenge that future projections of greenhouse gas concentrations are required before the ESM results can be 855 evaluated. In other words, the best estimate of future CO2 concentrations, given a certain emission pathway, will certainly differ at the end of the CMIP6 analysis cycle from the setting with which the MAGICC7 climate model was driven with for this study. This sequential problem could only be avoided with an altered experimental design, performing most future ESM experiments in an emission-driven, but computationally more demanding, design. An advantage of the concentration driven runs is that climate 860 feedbacks and carbon cycle feedbacks can more easily be separated.
In addition to the inconsistencies introduced by the sequential and concentration-driven nature of future climate scenario experiments, there are clearly limitations of MAGICC and its chosen default parameter settings for this study. A full evaluation of the extent to which the chosen parameters yield a concentration response that is representative of the higher complexity atmospheric chemistry model projections that are 865 part of CMIP6 will be of key interest for future studies.

Variable natural emissions.
Except for the interactive carbon cycle, this study assumes constant natural emissions levels for substances like CH4, N2O, CH3Br, CH3Cl and others. This is clearly a limitation, as under climate change and human management of the land and ocean, the magnitude of these natural emissions (indirectly 870 influenced by human activities) will change over time. Future research could build knowledge of the timevarying natural emission sources into the projection model used.

No uncertainty estimates
A major limitation of our study is the lack of uncertainty estimates. Given the primary purpose of this study of providing a single reference concentration projection as input dataset for the CMIP6 experiments, 875 uncertainty ranges around the projections are not necessary. However, in multiple other potential applications of this dataset, properly derived uncertainty information could have opened up new use cases.
For example, simple inversion studies could attempt to derive seasonally varying sink and source patterns from our observationally based historical monthly and latitudinally resolved concentration patterns.
Without the appropriate uncertainty information, any inversion approach will have to make ad-hoc 880 assumptions.

Conclusion
The projected human-induced increase of atmospheric greenhouse gas abundances over the 21 st century swamps all observed variations for the last 2000 years (Figure 8). The new SSP scenarios span an even broader range of CO2 concentration futures, with the higher end (SSP5-8.5) yielding higher concentrations 885 than the previous RCP8.5 scenario and the lower end SSP1-1.9 scenario resulting in CO2 emissions down to 350 ppm in the longer term (2150). Also, in a more technical aspect, the SSP concentrations are breaking new ground. For the first time, the greenhouse gas projections are available for 43 greenhouse gases, with latitudinal and seasonal variations captured. For example, by 2050, Northern hemispheric concentrations in the SSP3-7.0 scenario are 1.2% and 4.3% higher than Southern hemispheric averages 890 for CO2 and CH4, respectively -with corresponding non-negligible implications for radiative forcing (Table 5).
Given the substantial efforts that go into the data collection by observational network communities, a worthwhile effort in continuation from the present study would be to build a real-time framework to provide a system that updates GHG historical and future projections, including uncertainties, for a wide 895 range of -perhaps also updated -scenarios from the integrated assessment community. While updates of observations, gas cycle models or emission scenarios in between the major IPCC or WMO Assessments are useful for a range of scientific studies, the new GHG projections data could be frozen every several years to provide a new range of benchmark scenarios for Earth System Models. Efforts to provide more frequent updates for emissions data are also underway (e.g. Hoesly et al., 2018). 900 More than 20 years ago, the IPCC started to put forward future concentration scenarios, the so-called IS-  Table 1 -Derivation and construction of future CMIP6 mixing ratio fields for the greenhouse gas concentration series from 2015 onwards. Note that in addition to the steps shown below, a post-processing step was implemented to scale any differences in the December 2014 values between the raw future data and the previously submitted historical greenhouse gas concentration data. Those data differences in monthly latitudinal values for Dec 2014 were linearly scaled to zero until Dec 2015 in order to provide for a smooth transition between historical and future datasets 965 (section 2.8). See section 2.1 for a description of how the observational data was updated.

Gas
Time period Observational data source Global and annualmean ! !"#$%"    air mole fractions of CO2, CH4, N2O -reflecting the Oslo Line-by-line model results. This table can be compared to  Table 1 in Etminan et al. (2016), but note that their formulae can be directly applied to any sets of (C, Co), (M, Mo) and (N, No) within the range of fitting, unlike the case here where Co, Mo and No are pre-specified at pre-industrial levels.