Skip to main content

Main menu

  • Home
  • Articles
    • Current
    • Special Feature Articles - Most Recent
    • Special Features
    • Colloquia
    • Collected Articles
    • PNAS Classics
    • List of Issues
    • PNAS Nexus
  • Front Matter
    • Front Matter Portal
    • Journal Club
  • News
    • For the Press
    • This Week In PNAS
    • PNAS in the News
  • Podcasts
  • Authors
    • Information for Authors
    • Editorial and Journal Policies
    • Submission Procedures
    • Publication Charges
  • Submit
  • Submit
  • About
    • Editorial Board
    • PNAS Staff
    • FAQ
    • Accessibility Statement
    • Rights and Permissions
    • Site Map
  • Contact
  • Journal Club
  • Subscribe
    • Subscription Rates
    • Subscriptions FAQ
    • Open Access
    • Recommend PNAS to Your Librarian

User menu

  • Log in
  • My Cart

Search

  • Advanced search
Home
Home
  • Log in
  • My Cart

Advanced Search

  • Home
  • Articles
    • Current
    • Special Feature Articles - Most Recent
    • Special Features
    • Colloquia
    • Collected Articles
    • PNAS Classics
    • List of Issues
    • PNAS Nexus
  • Front Matter
    • Front Matter Portal
    • Journal Club
  • News
    • For the Press
    • This Week In PNAS
    • PNAS in the News
  • Podcasts
  • Authors
    • Information for Authors
    • Editorial and Journal Policies
    • Submission Procedures
    • Publication Charges
  • Submit
Research Article

Disentangling the effects of CO2 and short-lived climate forcer mitigation

Joeri Rogelj, Michiel Schaeffer, Malte Meinshausen, Drew T. Shindell, William Hare, Zbigniew Klimont, View ORCID ProfileGuus J. M. Velders, Markus Amann, and Hans Joachim Schellnhuber
  1. aInstitute for Atmospheric and Climate Science, ETH Zurich, 8092 Zürich, Switzerland;
  2. bEnergy Program and Mitigation of Air Pollution & Greenhouse Gases Program, International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria;
  3. cClimate Analytics gGmbH, 10969 Berlin, Germany;
  4. dEnvironmental Systems Analysis Group, Wageningen University and Research Centre, 6700 AA Wageningen, The Netherlands;
  5. eSchool of Earth Sciences, The University of Melbourne, 3010 Melbourne, VIC, Australia;
  6. fPotsdam Institute for Climate Impact Research, 14412 Potsdam, Germany;
  7. gNicholas School of the Environment, Duke University, Durham, NC 27708;
  8. hNational Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands; and
  9. iSanta Fe Institute, Santa Fe, NM 87501

See allHide authors and affiliations

PNAS November 18, 2014 111 (46) 16325-16330; first published November 3, 2014; https://doi.org/10.1073/pnas.1415631111
Joeri Rogelj
aInstitute for Atmospheric and Climate Science, ETH Zurich, 8092 Zürich, Switzerland;
bEnergy Program and Mitigation of Air Pollution & Greenhouse Gases Program, International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected] [email protected]
Michiel Schaeffer
cClimate Analytics gGmbH, 10969 Berlin, Germany;
dEnvironmental Systems Analysis Group, Wageningen University and Research Centre, 6700 AA Wageningen, The Netherlands;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Malte Meinshausen
eSchool of Earth Sciences, The University of Melbourne, 3010 Melbourne, VIC, Australia;
fPotsdam Institute for Climate Impact Research, 14412 Potsdam, Germany;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Drew T. Shindell
gNicholas School of the Environment, Duke University, Durham, NC 27708;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
William Hare
cClimate Analytics gGmbH, 10969 Berlin, Germany;
fPotsdam Institute for Climate Impact Research, 14412 Potsdam, Germany;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zbigniew Klimont
bEnergy Program and Mitigation of Air Pollution & Greenhouse Gases Program, International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Guus J. M. Velders
hNational Institute for Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Guus J. M. Velders
Markus Amann
bEnergy Program and Mitigation of Air Pollution & Greenhouse Gases Program, International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hans Joachim Schellnhuber
fPotsdam Institute for Climate Impact Research, 14412 Potsdam, Germany;
iSanta Fe Institute, Santa Fe, NM 87501
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected] [email protected]
  1. Contributed by Hans Joachim Schellnhuber, October 2, 2014 (sent for review June 4, 2014)

  • Article
  • Figures & SI
  • Info & Metrics
  • PDF
Loading

Significance

Climate change is one of the greatest challenges of our times. Human activities, like fossil-fuel burning, result in emissions of radiation-modifying substances that have a detectable, either warming or cooling, influence on our climate. Some, like soot (black carbon), are very short lived, whereas others, like carbon dioxide (CO2), are very persistent and remain in the atmosphere for centuries to millennia. Importantly, these substances are often emitted by common sources. As climate policy is looking at options to limit emissions of all these substances, understanding their linkages becomes extremely important. Our study disentangles these linkages and therewith helps to avoid crucial misconceptions: Measures reducing short-lived climate forcers are complementary to CO2 mitigation, but neglecting linkages leads to overestimating their climate benefits.

Abstract

Anthropogenic global warming is driven by emissions of a wide variety of radiative forcers ranging from very short-lived climate forcers (SLCFs), like black carbon, to very long-lived, like CO2. These species are often released from common sources and are therefore intricately linked. However, for reasons of simplification, this CO2–SLCF linkage was often disregarded in long-term projections of earlier studies. Here we explicitly account for CO2–SLCF linkages and show that the short- and long-term climate effects of many SLCF measures consistently become smaller in scenarios that keep warming to below 2 °C relative to preindustrial levels. Although long-term mitigation of methane and hydrofluorocarbons are integral parts of 2 °C scenarios, early action on these species mainly influences near-term temperatures and brings small benefits for limiting maximum warming relative to comparable reductions taking place later. Furthermore, we find that maximum 21st-century warming in 2 °C-consistent scenarios is largely unaffected by additional black-carbon-related measures because key emission sources are already phased-out through CO2 mitigation. Our study demonstrates the importance of coherently considering CO2–SLCF coevolutions. Failing to do so leads to strongly and consistently overestimating the effect of SLCF measures in climate stabilization scenarios. Our results reinforce that SLCF measures are to be considered complementary rather than a substitute for early and stringent CO2 mitigation. Near-term SLCF measures do not allow for more time for CO2 mitigation. We disentangle and resolve the distinct benefits across different species and therewith facilitate an integrated strategy for mitigating both short and long-term climate change.

  • climate change mitigation
  • air pollution
  • short-lived climate forcers
  • carbon dioxide
  • black carbon

For about two decades, policy-makers have considered options to avoid dangerous anthropogenic interference with the climate system (1). So far, many countries support limiting warming to below a 2 °C temperature limit, but the required global mitigation action to achieve this has been limited (2⇓–4). To inform policy-makers about options and challenges, the United Nations Environment Program (UNEP) published several reports over the past years on three interlinked aspects: climate stabilization and greenhouse gas (GHG) mitigation (3), short-lived climate forcers (SLCFs) and clean-air benefits (5, 6), and hydrofluorocarbons (7) (HFCs). We build here upon the insights of these reports (henceforth referred to as “Gap Report,” “SLCF Reports,” and “HFC Report,” respectively) to disentangle the joint effects of CO2 and SLCF mitigation for limiting global warming. We evaluate the potential for limiting global-mean warming until 2100 and the rate of near-term warming, with a focus on 2 °C-consistent scenarios (Fig. 1). Reductions in CO2 and SLCFs also provide important cobenefits like energy security (8), and local health and agricultural benefits (9⇓⇓–12), which fall outside the scope of this paper.

Fig. 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 1.

Influence of SLCF-CO2 linkages under varying CO2 mitigation. (A) Global-mean surface temperature implications and interdependence of CO2 (black), CH4 (green), HFC (orange), BC-related (blue), and SO2 mitigation (red). (B) The general effect of SLCF-CO2 linkages. CO2 paths show a world “with CO2 mitigation” (32) and with “no CO2 mitigation” (24). Early CH4 mitigation is represented by the combined light and dark green area. HFC mitigation is shown for the lower end of the range assessed in this study. BC-related (and SO2) measures show the difference between Case 6 and Case 2 (Case 4 and Case 2). Alternative cases are provided in SI Appendix, Fig. S1. Vertical dashed lines are time points relevant to Figs. 2 and 3.

The main challenge in this exercise is the interdependence of coemitted climate forcers and the differences between their net forcing effects (13). For example, energy-related black carbon (BC) aerosols have an overall warming effect (14), whereas sulfate aerosols and some biomass-related BC emissions together with their coemitted species are cooling (13, 14). Because CO2 and BC-related emissions often have common combustion sources (14), CO2 mitigation will also influence the abundance of SLCFs. This linkage has already been well studied for other air pollutants (15, 16). Due to data limitations, the first studies that analyzed the mitigation potential of SLCFs (5, 6, 9, 17⇓–19) did not account for these linkages in the long term and kept post-2030 SLCF forcing constant across a wide range of CO2 paths. Alternatively, simple relationships between species were used (20). Such approaches, however, cannot guarantee that the long-term SLCF and CO2 evolutions remain internally consistent. To provide an integrated view, we here account for this linkage and apply relationships (21) derived from detailed energy–environment–economy scenarios that explore various levels of air pollution control and track technological linkages between SCLF and CO2 sources (8). Each CO2 scenario in our analysis is thus associated with a consistent evolution of SLCFs at a specific level of pollution control stringency (see below). In policy discussions, methane (CH4) and BC are often subsumed under the single term “short-lived climate pollutants” (SLCP) but in light of their different influence on the climate, as well as differing technological and policy instruments for mitigation, they are explicitly distinguished here.

Our Analysis Framework

We approach our research question by modifying the emissions for BC-related SLCFs, HFCs, and CH4 in the scenarios from ref. 22 in a structured and internally consistent way. For BC-related SLCFs, several cases are created (Table 1, Methods, and SI Appendix, SI Text 1) following the approach described in ref. 21. Our “reference” (Case 1) assumes air pollution controls (8, 23) at the level of current legislation by 2030, and a worldwide convergence, along with economic affluence, to current levels of industrialized countries thereafter (8, 23⇓–25). We also assume gradual improvements over the next fifty years with respect to access to clean energy for the poor (26), long-term transitions to new energy technologies (25), and account for cocontrol in case of CO2 mitigation, resulting in a large share of the mitigation assumed by the SLCF Reports to be achieved at some point in the second half of the century.

View this table:
  • View inline
  • View popup
Table 1.

Description of BC-related SLCF cases analyzed in this study

Our “early measures” (Case 2) mimic implementation of the full package of BC-related measures of the SLCF Reports by 2030, and maximum feasible reductions for BC afterward (SI Appendix, Table S5; “maximum feasible reductions” assume best practice technologies of today to be implemented globally, ref. 8). This case also assumes no further measures that would reduce polluting but cooling species, like sulfur-dioxide (SO2) or nitrogen-oxides (NOx), beyond what is already assumed in their “reference” projections (Case 1). Measures in this package were selected based on their potential to reduce warming (6, 9). Many other air pollution control measures are available (including BC-related measures), yet would result in a smaller decrease or possibly increase in warming (14). The package of BC-related measures assessed in this study thus represents a high-end estimate of the potential influence of BC-related measures.

The influence of alternative reference levels and timing of measures is explored in four sensitivity cases: a 20-y “delay” in implementation of Case 2 (Case 3); “stringent SO2 controls” together with Case 2 (Case 4); a “frozen legislation” case with no air pollution control improvements beyond 2005 (Case 5); and a case without policies that promote access to clean energy for poor populations (no energy access policies; Case 6).

Because HFCs and CH4 are part of the Kyoto-GHG basket, multigas approaches (27) take into account these species together with CO2, but are often criticized from a long-term climate protection perspective (28⇓–30). We here do not follow this basket approach, but disentangle the suitability of the respective species for reducing near and long-term warming.

CH4 only has a few sources that are linked to, and thus possibly affected by, CO2 mitigation (e.g., CH4 release from fossil-fuel extraction). For each scenario in our set, we construct reference CH4 emissions that take into account this weak linkage (SI Appendix, SI Text 2) and are consistent with recent estimates (25, 31). We then compare these to a strong mitigation path (32) (RCP2.6). RCP2.6 reduces CH4 emissions from energy and waste, but also from agriculture (32), generally considered much harder (33), and represents the low end of CH4 mitigation scenarios (31) (SI Appendix, Fig. S3).

HFC emissions (34⇓–36) are projected to continue growing, especially in countries with emerging economies and increasing populations (34). They are part of the Kyoto-GHG basket, but discussions are under way to regulate them under the Montreal Protocol. Our HFC reference cases (34) reflect the high end of the literature (35), and the mitigation case reflects emissions in line with the SRES scenarios (37) (SI Appendix, SI Text 3 and Fig. S12).

The combination of our BC-related cases captures the SLCF Reports’ ranges (SI Appendix, Table S5) and for the same emission reductions, total radiative forcing simulated by our climate model changes consistently with earlier studies (17) and the SLCF Reports (9). Present-day forcing of BC was updated based on recent estimates (14, 38) that are considerably higher than earlier ones (13) (SI Appendix, SI Text 4, Fig. S11, and Table S6).

Effect on Absolute Temperatures

Maximum temperature increase (peak warming) is to first order determined by the cumulative emissions of long-lived GHGs until the peak (39⇓–41), and by the annual emissions of SLCFs at the time of the peak (42). We here assess the influence of measures on temperature increase until 2100, but note that temperatures will continue to rise in scenarios with positive nonzero CO2 emissions (43) in 2100.

For HFCs, we find that if the assumed increase in baseline emissions in developing countries (34) is not abated, maximum warming until 2100 can increase an additional 0.1–0.3 °C (Fig. 2B). For CH4, global-mean warming decreases by 0.3–0.7 °C by 2100 when moving from no to stringent CH4 mitigation (32) (median estimates dependent on concurrent CO2 mitigation, Fig. 2 B and C). CH4 mitigation measures in the latter half of the century become important if CO2 emissions have already been curbed, and warming thus peaks before 2100. Early action on CH4 is less important for limiting warming to below 2 °C: also when delaying CH4 reductions by three decades, a similar effect on maximum warming during the 21st century remains (Fig. 2B) (30, 41).

Fig. 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 2.

Influence of various SLCF measures on global-mean warming by 2030 (A) and on maximum warming until 2100 (B), as a function of maximum warming until 2100 relative to preindustrial levels under the reference scenario (Case 1). (C) Maximum temperature increase until 2100 as a function of cumulative CO2 emissions between 2000 and 2100. Scenarios at the left of the “2°C-consistent scenarios” line limit warming to below 2 °C with at least 50% probability. The effect of SLCF measures is smaller in stringent CO2 mitigation scenarios. Variation along each colored line is entirely driven by CO2 mitigation and technological SLCF linkages. Dots represent the median response per scenario (vertical gray lines: 90% range). Solid lines and numbers are quadratic fits and associated R2 values for each case, respectively. Pink ranges are defined by the quadratic fits for the HFC estimates. Additional cases and metrics are shown in SI Appendix, Figs. S4-S6.

Looking at BC-related measures (i.e., measures that reduce BC and its coemitted species), the influence of early measures (Case 2) on maximum 21st-century warming is small compared with our reference (Case 1). Maximum 21st-century warming is reduced by less than 1% (<<0.02 °C, about an order of magnitude smaller than natural variability in the climate system; Fig. 2B). This small reduction is due to similar emission levels in the long term, which are much lower than the levels suggested by studies that did not yet account for long-term CO2–SLCF linkages (9, 18). The influence of BC-related measures critically depends on how much concurrent CO2 mitigation is assumed and the timeframe considered. For instance, the cooling influence of BC-related measures is larger in the near-term (0.05–0.11 °C by 2030; Fig. 2A) and is largest in scenarios with little to no CO2 mitigation, which, even when taking into account this largest cooling due to BC measures, still have the highest medium and long-term warming. Delaying BC-related measures (Case 3) results in similarly small effects (Fig. 2B).

The effect of BC-related measures on maximum warming is thus limited, because scenarios that stabilize temperatures always require zero (or negligibly small) anthropogenic CO2 emissions for temperatures to peak (40). As a large fraction (55–65%) of the energy-related BC emissions with the largest net warming effect (14) are linked to CO2-emitting fossil-fuel sources, they also decline in low-carbon scenarios, also in the near term. The reference level of BC-related emissions is thus lowered as a cobenefit from CO2 mitigation, and achieving BC-related mitigation in 2 °C-consistent scenarios hence requires less additional reductions in comparison with scenarios that do not curb temperatures.

The robustness of our findings is illustrated by two sensitivity cases. Our frozen legislation (Case 5) explores the effect of more pessimistic air pollution control assumptions in line with the SLCF Reports’ reference. This case results in significantly higher BC emissions by 2030 (SI Appendix, Tables S4 and S5). However, the effect on maximum 21st-century warming remains small in 2 °C-consistent scenarios (<0.05 °C, Case 5 vs. 1; SI Appendix, Figs. S4 and S5) because also here BC reference levels are lowered due to the phase-out of common CO2-emitting sources (21). Not accounting for CO2–SLCF linkages would overestimate possible mitigation effects of BC-related measures in 2030 by about 50% (SI Appendix, Figs. S4–S8).

Not all SLCF emissions are cocontrolled by CO2 mitigation. Although about 70% of global BC emissions in the industrial era are related to energy use, the remainder is related to open burning (14) (e.g., from grassland and woodland fires). Of the energy-related BC emissions, 35–45% result from the residential use of traditional biomass (14), which is often considered carbon-neutral in integrated assessment models. These sources are therefore not cocontrolled in CO2 mitigation scenarios, but nevertheless decline in projections due to policies that promote access to clean energy. However, also when assuming no energy access policies (Case 6) over the 21st century, maximum warming by 2100 does not increase much (0.04–0.09 °C; SI Appendix, Figs. S4 and S5). Despite affecting a large share of BC-related emissions, the climate effect of energy access policies is assessed to be small (44) because the net forcing of BC and coemitted (reflecting) SLCFs from biomass burning is only slightly positive (14). Recent laboratory measurements (45) and modeling studies (46), however, suggest that this effect might be higher. Finally, policies that increase residential biomass use in industrialized countries (or the share of diesel in transport) can result in higher SLCF emissions, unless appropriate control measures are adopted.

Our cases show the importance of accounting for CO2–SLCF linkages. In a “no CO2 mitigation” world (Fig. 1) the maximum temperature influence in 2100 by CH4, HFCs and BC measures is about 0.7 °C, 0.2 °C, and 0.1 °C, respectively, adding up to a combined effect of about 0.9 °C. This differs markedly from a world “with CO2 mitigation,” where the influence declines to 0.4 °C, 0.1 °C and <0.05 °C, respectively, adding up to about 0.5 °C in 2100. Our study thus reveals that not accounting for CO2–SLCF linkages can lead to overestimating the temperature effect of the combined SLCF mitigation measures by almost 100% (with important differences across various SLCF species). For comparison, our combined “no CO2 mitigation” estimate for 2100 is approximately consistent, given uncertainty bounds, with the effect estimated by earlier studies, like 1.1 °C in ref. 17. However, this changes once CO2–SLCF linkages are accounted for. Assuming constant extrapolated values after 2030 for all BC-related emissions (5, 6, 9, 17⇓–19) would suggest a near-constant lowering of long-term warming (0.2–0.25 °C) consistent with very high temperature scenarios (see SI Appendix, SI Text 6 for a detailed comparison). By 2100, this effect is up to a factor two to four larger than the maximum found in 2 °C-consistent scenarios (i.e., for our two sensitivity cases that have the highest pollution loading combined, SI Appendix, Figs. S4 and S5), and this discrepancy exacerbates to an order of magnitude when using current legislation (Case 1) as the reference. As a consequence, our results invalidate suggestions that BC-related measures would allow higher near-term (2020) Kyoto-GHG emissions (5) in line with staying below 2 °C, or allow for more time for CO2 reductions (47) (SI Appendix, Table S2). Our measures case (Case 2) assumes that no additional efforts are made to control cooling SO2 emissions beyond cocontrol by CO2 mitigation strategies. Because dominant sources of SO2 (48) and BC (9, 14) are not the same, SO2 emissions will not be significantly reduced by BC-related measures. However, as SO2 contributes to the formation of acid rain and has adverse local health effects by forming secondary aerosols (49), public-health concerns drive additional near-term reductions. Such reductions then unmask warming induced by other species (50). If we assume stringent SO2 controls (assuming current best practice technologies to be implemented globally by 2030), the unmasking of warming due to SO2 removal is larger than the cooling effect of the our BC-related measures package, resulting in a net temperature increase (Fig. 2, red vs. black lines).

The main contributors to maximum 21st-century warming are long-lived GHGs, of which the most important is CO2 (41, 43). When varying trajectories of CO2 emissions up to 2050 from less to more stringent reduction measures over a range comparable with the SLCF measures (SI Appendix, SI Text 5), maximum 21st-century warming varies by more than 2.5 °C (SI Appendix, Fig. S6). For most scenarios in our set warming peaks after 2100 (SI Appendix, Fig. S7), making relative contributions of SLCF measures to peak warming increasingly smaller over time (41).

Rates of Temperature Change

We also assess implications for the change in average decadal rates of temperature change (ARTCs) between 2010–2030, 2030–2050, and 2010–2050. The ARTCs over our scenario set in all three periods are ∼0.23 °C per decade (Fig. 3 D and E).

Fig. 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
Fig. 3.

Influence of SLCF and CO2 mitigation on ARTCs between 2010–2030 (A) and 2030–2050 (B) as a function of maximum warming until 2100 relative to preindustrial levels under the reference scenario (Case 1). (C) ARTCs between 2030–2050 as a function of CO2 emissions in 2050. (D and E) Frequency distributions of ARTCs between 2010–2030 and 2030–2050, respectively, together with mean estimates (vertical solid lines) over the entire ensemble. Each dot represents the median response per scenario. Solid lines and numbers in A–C are quadratic fits and associated R2 values for each case, respectively. Pink ranges in A–C are defined by the quadratic fits for the range of HFC estimates. Additional cases are shown in SI Appendix, Figs. S8 and S9.

The potential influence on ARTCs of the projected post-2020 HFC emissions becomes visible after 2030. ARTCs rise by about 10–20% and 5–10% between 2030–2050 and 2010–2050, respectively (Fig. 3, rounded to the nearest 5%). Our stringent CH4 mitigation case reduces ARTCs by about 20% between 2010–2030, by about 25–40% between 2030–2050, and by about 20–30% between 2010–2050. For BC, we find that ARTCs are reduced at the time that the reductions of Case 2 take place (10–20% by 2030). However, they are increased by about 5% between 2030–2050, at the time when emissions would otherwise have declined in the reference case. This results in a small overall reduction between 2010–2050 (about 5–10%; SI Appendix, Fig. S8). When assuming frozen legislation as the reference, ARTCs between 2030–2050 can either increase or decrease depending on the concurrent CO2 mitigation (SI Appendix, Figs. S8 and S9). This finding thus highlights the importance of accounting for CO2–SLCF linkages.

Also, changes in CO2 emissions influence rates of temperature change (43, 51). We here explore the effect of reducing CO2 emissions while accounting for technologically linked SLCF-reductions. On shorter time scales (until 2030), the effect on temperature rates is virtually zero. However, limiting cumulative CO2 emissions until 2050 to 2 °C-consistent levels (<350 PgC, SI Appendix, Fig. S6B) leads to ARTCs between 2030–2050 of about 0.15 °C/decade instead of about 0.35 °C/decade when emissions are on track for 4 °C (∼700 PgC), a shift of more than 50% (SI Appendix, Fig. S9B). Path dependency due to lock-in of carbon-intensive infrastructure constrains attainable emission reduction rates (52) and early measures to reduce CO2 are thus required to significantly limit cumulative emission by 2050. For each 5 PgC/y that annual CO2 emission targets are set lower for 2050, ARTCs between 2030–2050 (2010–2050) decline by about 15% (10%, Fig. 3C).

Discussion and Conclusions

For around a decade, scholars have been discussing SLCFs and CO2 mitigation in relation to combating climate change (17, 53⇓–55), with two seminal papers (17, 54) identifying SLCFs as a way to mitigate short-term warming. Our results provide an integrated view and quantitatively support earlier statements (9, 17) that mitigation of SLCFs can only be a complementary strategy on top of CO2 mitigation, but also reveal distinct benefits across different SLCFs and highlight the importance of a coherent consideration of dependencies between SLCFs and CO2.

Eventual CH4 mitigation forms an integral part of long-term climate protection strategies, and also the potential increase of HFCs requires attention in the long run. Although early CH4 and BC-related measures reduce the rate of temperature rise in the coming two decades, early action to limit SLCFs by 2030 brings only small benefits insofar as peak warming goes. Deep CH4 reductions help hedging the risk of exceeding temperature thresholds (52, 56), yet only when CO2 reductions are already put in place (19). The effects of CH4 and HFC measures are robust across a wide range of CO2 scenarios. However, when accounting for CO2–SLCF linkages in scenarios that stabilize global warming, long-term effects of BC-related measures become virtually zero. Earlier studies also found reduced effects because of uncertainties in aerosol emissions and forcing (31), or found that forcing estimates lower than those applied here would be more consistent with observations (57). Other studies (45, 46), however, indicate that the forcing effect of biomass burning might have been underestimated in the past. Caution is therefore advised.

Delaying stringent action on CO2 results in lock-in of carbon-emitting infrastructure (52) and higher cumulative CO2 emissions that imply a higher committed warming. Because of this, and the persistence of CO2 in the atmosphere, near-term initiation of CO2 mitigation is required to control midcentury to long-term climate change. Replacing near-term CO2 reductions with SLCF mitigation leads to a higher risk that stabilization of concentration and warming is not achieved (28⇓–30, 52, 56). Even when action on CO2 continues to be delayed, the effect of our package of BC-related measures is smaller than previously estimated (SI Appendix, Fig. S10). These results imply that SLCF measures are not able to buy substantial time for CO2 action, and our study therewith rectifies a misconception present in the policy literature (47), despite multiple studies already having warned against such interpretation (6, 9, 17, 19, 29, 41).

The package of BC-related emission reduction measures in this paper represents a high-end estimate of BC-related climate mitigation, in line with the SLCF Reports (5, 6, 9). This package is currently promoted to spur momentum for international climate collaboration (47), together with action on CH4 and HFCs. Our analysis shows that lumping all SLCF measures in one category would obscure many of the important differences between the species. Moreover, imposing air pollution controls on cooling SO2 emissions significantly reduce the overall temperature effect by 2030. Meanwhile, at current CO2 emission rates of ∼10 PgC/y (4), each decade of delayed CO2 mitigation implies around 0.17 °C further warming over multiple centuries [Fig. 2C; the IPCC estimate (58) for similar CO2-only emissions is 0.08–0.25 °C]. In none of our cases can BC-related measures compensate for the persistent impacts of unabated CO2 emissions. Without early and stringent CO2 mitigation, warming from 2050 onward will become increasingly larger than what SLCF measures can reduce.

Achievement of the BC-related emissions reductions assessed in this study has important benefits beyond near-term climate protection (e.g., for public health). These other benefits can provide a valid rationale for early implementation, and will require dedicated and sustained policy interventions, whether through accelerated implementation of air pollution controls, through cocontrol due to stringent CO2 mitigation strategies, or by promoting access to clean energy for poor populations in developing countries. CH4 and CO2 mitigation provide also multiple other benefits.

The results presented here are consistent with the earlier UNEP Reports and underlying studies (9, 22, 34) but only in the near term (2030) and when assuming frozen legislation as the reference policy in scenarios with little to no CO2 mitigation (SI Appendix, SI Text 6). In the long term (2050 and beyond) and for stringent CO2 mitigation scenarios, we find only modest effects of SLCF reductions, even compared with our sensitivity cases with the highest loading of pollutant emissions. Our results robustly demonstrate that not accounting for cocontrol due to SLCF-CO2 linkages in a low-carbon world leads to strongly overestimating the long-term effect of BC-related measures. By disentangling the distinct benefits across different species in time, our results provide a robust basis for an integrated strategy for mitigating both short and long-term climate change.

Methods

We use the reduced-complexity carbon-cycle and climate model MAGICC (59) in a probabilistic setup (39) updated such that the marginal climate sensitivity distribution is consistent with IPCC AR4 (60); for AR5 consistency, see ref. 61. Temperature increase relative to preindustrial (1850–1875) is computed from a 600-member ensemble (39). Our setup is closely in line with historical radiative forcing estimates of IPCC AR4 (13) and has been updated to reflect the most recent BC forcing estimates (14), included in IPCC AR5 (SI Appendix, SI Text 4 and Table S6). Reported results are robust for a wide range of climate sensitivity estimates (SI Appendix, Fig. S13). Emissions in our scenarios have been harmonized (62) with recent inventories of historical emissions (63, 64).

BC-related cases are described in Table 1, SI Appendix, SI Text 1 and Table S1. Sectors that are not affected by the measures (for example, forest and savannah burning) do not vary between scenarios. HFC cases are described in SI Appendix, SI Text 3.

Acknowledgments

We thank Piers M. de F. Forster for his help in updating our analysis to new forcing estimates; Reto Knutti, Veerabhadran Ramanathan, Joseph Alcamo, and Markus Huber for feedback on the manuscript; and Jan Sedláček for providing natural variability estimates from the CMIP5 archive.

Footnotes

  • ↵1To whom correspondence may be addressed. Email: john{at}pik-potsdam.de or rogelj{at}iiasa.ac.at.
  • Author contributions: J.R., M.S., D.T.S., and W.H. designed research; J.R. coordinated the analysis; J.R., M.S., and H.J.S. performed research; M.M. developed the setup of the MAGICC model; J.R. updated the MAGICC model setup; and J.R., M.S., M.M., D.T.S., W.H., Z.K., G.J.M.V., M.A., and H.J.S. wrote the paper.

  • The authors declare no conflict of interest.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1415631111/-/DCSupplemental.

Freely available online through the PNAS open access option.

References

  1. ↵
    1. Schellnhuber HJ,
    2. Cramer W,
    3. Nakicenovic N,
    4. Wigley TML,
    5. Yohe G
    (2006) Avoiding Dangerous Climate Change (Cambridge Univ Press, Cambridge, UK), p 392
    .
  2. ↵
    1. Rogelj J, et al.
    (2010) Copenhagen Accord pledges are paltry. Nature 464(7292):1126–1128
    .
    OpenUrlCrossRefPubMed
  3. ↵
    1. UNEP
    (2011) Bridging the Emissions Gap (UNEP, Nairobi, Kenya), pp 56
    .
  4. ↵
    1. Friedlingstein P, et al.
    (2014) Persistent growth of CO2 emissions and implications for reaching climate targets. Nat Geosci, ; advance online publication
    .
  5. ↵
    1. UNEP
    (2011) Near-term Climate Protection and Clean Air Benefits: Actions for Controlling Short-Lived Climate Forcers (UNEP, Nairobi, Kenya), pp 78
    .
  6. ↵
    1. UNEP/WMO
    (2011) Integrated Assessment of Black Carbon and Tropospheric Ozone (UNEP/WMO, Nairobi, Kenya), pp 285
    .
  7. ↵
    1. UNEP
    (2011) HFCs: A Critical Link in Protecting Climate and the Ozone Layer (UNEP, Nairobi, Kenya), pp 36
    .
  8. ↵
    1. McCollum D, et al.
    (2013) Climate policies can help resolve energy security and air pollution challenges. Clim Change 119(2):479–494
    .
    OpenUrlCrossRef
  9. ↵
    1. Shindell D, et al.
    (2012) Simultaneously mitigating near-term climate change and improving human health and food security. Science 335(6065):183–189
    .
    OpenUrlAbstract/FREE Full Text
  10. ↵
    1. Anenberg SC, et al.
    (2012) Global air quality and health co-benefits of mitigating near-term climate change through methane and black carbon emission controls. Environ Health Perspect 120(6):831–839
    .
    OpenUrlCrossRefPubMed
  11. ↵
    1. Rao S, et al.
    (2012) Environmental Modeling and Methods for Estimation of the Global Health Impacts of Air Pollution. Environ Model Assess 17(6):613–622
    .
    OpenUrlCrossRef
  12. ↵
    1. West JJ, et al.
    (2013) Co-benefits of mitigating global greenhouse gas emissions for future air quality and human health. Nature Clim. Change 3(10):885–889
    .
    OpenUrlCrossRef
  13. ↵
    1. Forster P, et al.
    (2007) Changes in Atmospheric Constituents and in Radiative Forcing. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Chapter 2, eds Solomon S, et al. (Cambridge Univ Press, Cambridge, UK), pp 129–234
    .
  14. ↵
    1. Bond TC, et al.
    (2013) Bounding the role of black carbon in the climate system: A scientific assessment. J Geophys Res Atmos 118(11):5380–5552
    .
    OpenUrlCrossRef
  15. ↵
    1. Syri S, et al.
    (2001) Low-CO2 energy pathways and regional air pollution in Europe. Energy Policy 29(11):871–884
    .
    OpenUrlCrossRef
  16. ↵
    1. van Vuuren DP, et al.
    (2006) Exploring the ancillary benefits of the Kyoto Protocol for air pollution in Europe. Energy Policy 34(4):444–460
    .
    OpenUrlCrossRef
  17. ↵
    1. Ramanathan V,
    2. Xu Y
    (2010) The Copenhagen Accord for limiting global warming: Criteria, constraints, and available avenues. Proc Natl Acad Sci USA 107(18):8055–8062
    .
    OpenUrlAbstract/FREE Full Text
  18. ↵
    1. Hu A,
    2. Xu Y,
    3. Tebaldi C,
    4. Washington WM,
    5. Ramanathan V
    (2013) Mitigation of short-lived climate pollutants slows sea-level rise. Nature Clim. Change 3(8):730–734
    .
    OpenUrlCrossRef
  19. ↵
    1. Bowerman NHA, et al.
    (2013) The role of short-lived climate pollutants in meeting temperature goals. Nature Clim. Change 3(12):1021–1024
    .
    OpenUrlCrossRef
  20. ↵
    1. Huntingford C, et al.
    (2012) The link between a global 2 °C warming threshold and emissions in years 2020, 2050 and beyond. Environ Res Lett 7(1):014039
    .
    OpenUrlCrossRef
  21. ↵
    1. Rogelj J, et al.
    (2014) Air-pollution emission ranges consistent with the representative concentration pathways. Nature Clim. Change 4(6):446–450
    .
    OpenUrlCrossRef
  22. ↵
    1. Rogelj J, et al.
    (2011) Emission pathways consistent with a 2°C global temperature limit. Nature Clim. Change 1(8):413–418
    .
    OpenUrlCrossRef
  23. ↵
    1. Rao S, et al.
    (2013) Better air for better health: Forging synergies in policies for energy access, climate change and air pollution. Glob Environ Change 23(5):1122–1130
    .
    OpenUrlCrossRef
  24. ↵
    1. Riahi K, et al.
    (2011) RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Clim Change 109(1):33–57
    .
    OpenUrlCrossRef
  25. ↵
    1. Riahi K, et al.
    (2012) Chapter 17 - Energy Pathways for Sustainable Development. Global Energy Assessment - Toward a Sustainable Future (Cambridge Univ Press, Cambridge, UK, and IIASA, Laxenburg, Austria), pp 1203–1306
    .
  26. ↵
    1. Pachauri S, et al.
    (2012) Chapter 19 - Energy Access for Development. Global Energy Assessment - Toward a Sustainable Future (Cambridge Univ Press, Cambridge, UK, and IIASA, Laxenburg, Austria) pp 1401–1458
    .
  27. ↵
    1. Weyant JP,
    2. de la Chesnaye FC,
    3. Blanford GJ
    (2006) Overview of EMF-21: Multigas Mitigation and Climate Policy. Energy J (Camb Mass) 27(Special Issue):1–32
    .
    OpenUrl
  28. ↵
    1. Solomon S,
    2. Pierrehumbert R,
    3. Matthews D,
    4. Daniel J,
    5. Friedlingstein P
    (2013) Atmospheric composition, irreversible climate change, and mitigation policy. Climate Science for Serving Society - Research, Modeling and Prediction Priorities, eds Hurrell J, Asrar G (Springer, Dordrecht, The Netherlands), pp 415–436
    .
  29. ↵
    1. Myhre G,
    2. Fuglestvedt JS,
    3. Berntsen TK,
    4. Lund MT
    (2011) Mitigation of short-lived heating components may lead to unwanted long-term consequences. Atmos Environ 45(33):6103–6106
    .
    OpenUrlCrossRef
  30. ↵
    1. Shoemaker J,
    2. Schrag D
    (2013) The danger of overvaluing methane’s influence on future climate change. Clim Change 120(4):903–914
    .
    OpenUrlCrossRef
  31. ↵
    1. Smith SJ,
    2. Mizrahi A
    (2013) Near-term climate mitigation by short-lived forcers. Proc Natl Acad Sci USA 110(35):14202–14206
    .
    OpenUrlAbstract/FREE Full Text
  32. ↵
    1. van Vuuren D, et al.
    (2011) RCP2.6: Exploring the possibility to keep global mean temperature increase below 2°C. Clim Change 109(1):95–116
    .
    OpenUrl
  33. ↵
    1. Beach RH, et al.
    (2008) Mitigation potential and costs for global agricultural greenhouse gas emissions. Agric Econ 38(2):109–115
    .
    OpenUrlCrossRef
  34. ↵
    1. Velders GJM,
    2. Fahey DW,
    3. Daniel JS,
    4. McFarland M,
    5. Andersen SO
    (2009) The large contribution of projected HFC emissions to future climate forcing. Proc Natl Acad Sci USA 106(27):10949–10954
    .
    OpenUrlAbstract/FREE Full Text
  35. ↵
    1. Gschrey B,
    2. Schwarz W,
    3. Elsner C,
    4. Engelhardt R
    (2011) High increase of global F-gas emissions until 2050. GHG Measur. and Manag. 1(2):85–92
    .
    OpenUrl
  36. ↵
    1. TEAP
    (2009) Task Force Decision XX/8 Report, Assessment of Alternatives to HCFCs and HFCs and Update of the TEAP 2005 Supplement Report Data, eds Kuijpers L, Verdonik D (UNEP, Nairobi, Kenya)
    .
  37. ↵
    1. Nakicenovic N,
    2. Swart R
    (2000) IPCC Special Report on Emissions Scenarios (Cambridge Univ Press, Cambridge, UK), pp 570
    .
  38. ↵
    1. Ramanathan V,
    2. Carmichael G
    (2008) Global and regional climate changes due to black carbon. Nat Geosci 1(4):221–227
    .
    OpenUrlCrossRef
  39. ↵
    1. Meinshausen M, et al.
    (2009) Greenhouse-gas emission targets for limiting global warming to 2 ° C. Nature 458(7242):1158–1162
    .
    OpenUrlCrossRefPubMed
  40. ↵
    1. Matthews HD,
    2. Gillett NP,
    3. Stott PA,
    4. Zickfeld K
    (2009) The proportionality of global warming to cumulative carbon emissions. Nature 459(7248):829–832
    .
    OpenUrlCrossRefPubMed
  41. ↵
    1. Pierrehumbert RT
    (2014) Short-Lived Climate Pollution. Annu Rev Earth Planet Sci 42(1):341–379
    .
    OpenUrlCrossRef
  42. ↵
    1. Smith SM, et al.
    (2012) Equivalence of greenhouse-gas emissions for peak temperature limits. Nature Clim. Change 2(7):535–538
    .
    OpenUrl
  43. ↵
    1. Matthews HD,
    2. Solomon S,
    3. Pierrehumbert R
    (2012) Cumulative carbon as a policy framework for achieving climate stabilization. Phil Trans R Soc A 370(1974):4365–4379
    .
    OpenUrlAbstract/FREE Full Text
  44. ↵
    1. Rogelj J,
    2. McCollum DL,
    3. Riahi K
    (2013) The UN's 'Sustainable Energy for All' initiative is compatible with a warming limit of 2°C. Nature Clim. Change 3(6):545–551
    .
    OpenUrl
  45. ↵
    1. Saleh R, et al.
    (2014) Brownness of organics in aerosols from biomass burning linked to their black carbon content. Nat Geosci 7(9):647–650
    .
    OpenUrlCrossRef
  46. ↵
    1. Jacobson MZ
    (2014) Effects of biomass burning on climate, accounting for heat and moisture fluxes, black and brown carbon, and cloud absorption effects. J Geophys Res Atmospheres 119(14):2014JD021861
    .
    OpenUrl
  47. ↵
    1. Victor DG,
    2. Kennel CF,
    3. Ramanathan V
    (2012) The Climate Threat We Can Beat. Foreign Aff 91:112–121
    .
    OpenUrl
  48. ↵
    1. Klimont Z,
    2. Smith SJ,
    3. Cofala J
    (2013) The last decade of global anthropogenic sulfur dioxide: 2000–2011 emissions. Environ Res Lett 8(1):014003
    .
    OpenUrlCrossRef
  49. ↵
    1. Lim SS, et al.
    (2012) A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 380(9859):2224–2260
    .
    OpenUrlCrossRefPubMed
  50. ↵
    1. Chae Y,
    2. Hope C
    (2003) Integrated assessment of CO2 and SO2 policies in North East Asia. Climate Policy 3. Supplement 1(0):S57–S79
    .
    OpenUrl
  51. ↵
    1. Bowerman NHA,
    2. Frame DJ,
    3. Huntingford C,
    4. Lowe JA,
    5. Allen MR
    (2011) Cumulative carbon emissions, emissions floors and short-term rates of warming: Implications for policy. Phil Trans R Soc A 369(1934):45–66
    .
    OpenUrlAbstract/FREE Full Text
  52. ↵
    1. Rogelj J,
    2. McCollum DL,
    3. O'Neill BC,
    4. Riahi K
    (2013) 2020 emissions levels required to limit warming to below 2°C. Nature Clim Change 3(4):405–412
    .
    OpenUrl
  53. ↵
    1. Schellnhuber HJ
    (2008) Global warming: Stop worrying, start panicking? Proc Natl Acad Sci USA 105(38):14239–14240
    .
    OpenUrlFREE Full Text
  54. ↵
    1. Ramanathan V,
    2. Feng Y
    (2008) On avoiding dangerous anthropogenic interference with the climate system: Formidable challenges ahead. Proc Natl Acad Sci USA 105(38):14245–14250
    .
    OpenUrlAbstract/FREE Full Text
  55. ↵
    1. Jacobson MZ
    (2001) Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols. Nature 409(6821):695–697
    .
    OpenUrlCrossRefPubMed
  56. ↵
    1. Rogelj J,
    2. McCollum DL,
    3. Reisinger A,
    4. Meinshausen M,
    5. Riahi K
    (2013) Probabilistic cost estimates for climate change mitigation. Nature 493(7430):79–83
    .
    OpenUrlCrossRefPubMed
  57. ↵
    1. Hodnebrog O,
    2. Myhre G,
    3. Samset BH
    (2014) How shorter black carbon lifetime alters its climate effect. Nat Commun 5:5065
    .
    OpenUrlCrossRefPubMed
  58. ↵
    1. IPCC
    (2013) Summary for Policymakers. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, ed Stocker TF, et al. (Cambridge Univ Press, Cambridge, UK), pp 1–29
    .
  59. ↵
    1. Meinshausen M,
    2. Raper SCB,
    3. Wigley TML
    (2011) Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration. Atmos Chem Phys 11(4):1417–1456
    .
    OpenUrlCrossRef
  60. ↵
    1. Rogelj J,
    2. Meinshausen M,
    3. Knutti R
    (2012) Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nature Clim. Change 2(4):248–253
    .
    OpenUrlCrossRef
  61. ↵
    1. Rogelj J,
    2. Meinshausen M,
    3. Sedláček J,
    4. Knutti R
    (2014) Implications of potentially lower climate sensitivity on climate projections and policy. Environ Res Lett 9(3):031003
    .
    OpenUrlCrossRef
  62. ↵
    1. Rogelj J,
    2. Hare W,
    3. Chen C,
    4. Meinshausen M
    (2011) Discrepancies in historical emissions point to a wider 2020 gap between 2°C benchmarks and aggregated national mitigation pledges. Environ Res Lett 6(2):9
    .
    OpenUrl
  63. ↵
    1. Meinshausen M, et al.
    (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Change 109(1):213–241
    .
    OpenUrlCrossRef
  64. ↵
    1. Granier C, et al.
    (2011) Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period. Clim Change 109(1-2):163–190
    .
    OpenUrlCrossRef
PreviousNext
Back to top
Article Alerts
Email Article

Thank you for your interest in spreading the word on PNAS.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Disentangling the effects of CO2 and short-lived climate forcer mitigation
(Your Name) has sent you a message from PNAS
(Your Name) thought you would like to see the PNAS web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Disentangling CO2 and short-lived climate forcers
Joeri Rogelj, Michiel Schaeffer, Malte Meinshausen, Drew T. Shindell, William Hare, Zbigniew Klimont, Guus J. M. Velders, Markus Amann, Hans Joachim Schellnhuber
Proceedings of the National Academy of Sciences Nov 2014, 111 (46) 16325-16330; DOI: 10.1073/pnas.1415631111

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Request Permissions
Share
Disentangling CO2 and short-lived climate forcers
Joeri Rogelj, Michiel Schaeffer, Malte Meinshausen, Drew T. Shindell, William Hare, Zbigniew Klimont, Guus J. M. Velders, Markus Amann, Hans Joachim Schellnhuber
Proceedings of the National Academy of Sciences Nov 2014, 111 (46) 16325-16330; DOI: 10.1073/pnas.1415631111
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Mendeley logo Mendeley

Article Classifications

  • Physical Sciences
  • Sustainability Science
Proceedings of the National Academy of Sciences: 111 (46)
Table of Contents

Submit

Sign up for Article Alerts

Jump to section

  • Article
    • Abstract
    • Our Analysis Framework
    • Effect on Absolute Temperatures
    • Rates of Temperature Change
    • Discussion and Conclusions
    • Methods
    • Acknowledgments
    • Footnotes
    • References
  • Figures & SI
  • Info & Metrics
  • PDF

You May Also be Interested in

Protective infrastructure along the San Francisco Bay shoreline.
Economic impact of sea level rise protection
Infrastructure built to protect cities from flooding can increase economic damages elsewhere.
Image credit: Michelle A. Hummel.
Venus.
Abiotic source of phosphine on Venus
Phosphine in the Venusian atmosphere can be explained without biogenic sources and is consistent with ongoing volcanism on Venus.
Image credit: Wikimedia Commons/NASA.
Coronavirus.
Estimating true number of COVID-19 infections
A study finds underreporting of COVID-19 cases in the United States and that the United States is likely far from achieving herd immunity through infection alone.
Image credit: Pixabay/geralt.
Three test tubes with lethal doses of heroin, carfentanil, and fentanyl.
Inner Workings: Vaccines aim to fight drugs of abuse
Researchers hope vaccines can serve as a key tool for addressing the opioid epidemic. The first clinical trials are underway, though big challenges remain.
Image credit: United States Drug Enforcement Administration.
Factories belch pollution into a hazy sky as the sun peaks out from behind the clouds.
Journal Club: How to incorporate changing human behaviors into planetary models
Eyeing the effects of the Anthropocene, researchers offer a novel framework to identify and combine models from across the physical and social sciences.
Image credit: Shutterstock/Victor Lauer.

Similar Articles

Site Logo
Powered by HighWire
  • Submit Manuscript
  • Twitter
  • Youtube
  • Facebook
  • RSS Feeds
  • Email Alerts

Articles

  • Current Issue
  • Special Feature Articles – Most Recent
  • List of Issues

PNAS Portals

  • Anthropology
  • Chemistry
  • Classics
  • Front Matter
  • Physics
  • Sustainability Science
  • Teaching Resources

Information

  • Authors
  • Editorial Board
  • Reviewers
  • Subscribers
  • Librarians
  • Press
  • Cozzarelli Prize
  • Site Map
  • PNAS Updates
  • FAQs
  • Accessibility Statement
  • Rights & Permissions
  • About
  • Contact

Feedback    Privacy/Legal

Copyright © 2021 National Academy of Sciences. Online ISSN 1091-6490. PNAS is a partner of CHORUS, COPE, CrossRef, ORCID, and Research4Life.