THIS POST DESCRIBES THE USE OF EARTH SYSTEM MODELS (ESM) IN CLIMATE CHANGE RESEARCH AND EXPLORES THE LINK BETWEEN ESM AND TCR (TRANSIENT CLIMATE RESPONSE TO CUMULATIVE EMISSIONS)

QUESTION: WHAT IS AN EARTH SYSTEM MODEL

ANSWER: A very readable paper on ESMs by IPCC Climate Scientist Dr. Gregory Flato is available online. Here is the link: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.662.1789&rep=rep1&type=pdf This post is a further exploration of his answer to this question.

PART-1: THE ESSENTIAL THEORY OF AGW AND ITS WEAKNESS

Historically, the original theory of anthropogenic global warming (AGW) and climate change proposed since Callendar 1938 and found in Revelle 1957, Charney 1979, Hansen 1981, Hansen 1988, IPCC 1990, IPCC 1996, IPCC 2001, IPCC 2007, Lacis 2010, IPCC 2014 and others has been consistently and uniformly stated as follows:

(1) Since the Industrial Revolution humans have been digging up and burning old carbon from under the ground and releasing carbon that is millions of years old into the atmosphere.

(2) This carbon is not a part of the current account of he carbon cycle and therefore an external perturbation of the carbon cycle with very old carbon and this perturbation changes atmospheric composition and causes atmospheric CO2 concentration to rise. This rise is therefore artificial and it is the human cause element of AGW theory.

(3) The rate of rise in atmospheric CO2 concentration seen in the data from Mauna Loa imply that about half of the fossil fuel emissions remain in the atmosphere, the so called “Airborne Fraction” while the other half becomes absorbed into carbon cycle sinks such as photosynthesis and ocean uptake.

(4) The surface temperature of the earth is a logarithmic function of the CO2 concentration of the atmosphere due to the heat trapping greenhouse effect of CO2. Therefore, as atmospheric CO2 rises, so does global mean surface temperature. This rise in temperature is therefore human caused by way of fossil fuel emissions. This is what makes global warming ANTHROPOGENIC and why we call it AGW.

(5) The empirical evidence of this causation sequence is that the theory predicts and the data show that temperature is a linear function of the natural logarithm of atmospheric CO2 concentration with a regression line slope that implies equilibrium climate sensitivity or ECS. The ECS relates the amount of warming for each doubling of atmospheric CO2 concentration.

(6) In 1979, Jule Charney consolidated results from five climate models to report Equilibrium Climate Sensitivity variously as ECS=[2.0-3.5], ECS=[2.6-4.1], and ECS=[1.5-4.5]. Charney then declared without elaborating that the most likely value of the ECS = 3 with its uncertainty indicated by the range ECS=[1.5-4.5] (Charney, 1979). This range was adopted by the IPCC and has since become dogma in climate science.

(7) However, there are unsettled and troublesome issues in this apparently empirically validated theory. The most well known issue, and one acknowledged by climate science, is UNCERTAINTY– that is the theory says that there is an ECS, and we can find the non-zero and non-negative ECS in the empirical evidence but we don’t know what the value of the ECS is exactly.

(8) This state of confusion in ECS research is best understood in terms of the following empirical results where some are purely observational while others are observational values constrained by climate models or climate model values constrained by observations : (Andronova, 2000) [2.0-5.0] ECS=[0.94-2.35]. (Gregory, 2002) [1.7 – 2.3], [1.4 – 7.7]. (Knutti, 2002) [2.7 – 8.7]. (Frame, 2005) [1.4–4.1], (Murphy, 2004) = [2.4–5.4] , (Stainforth, 2005) [1.9 – 11.5]. (Hegerl, 2006) [1.5-6.2]. (Kummer, 2017) [ 1.6-4.1]. Other estimates are ECS ≈ [1.2 – 7.7] for unconstrained observational [1.0 – 2.7], [1.0 – 3.5], [1.0 – 4.2], [1.3 – 4.9], [1.5 – 7.8], [1.5 – 7.3], and [1.0 – 7.0]. for observational ECS constrained by models. A more complete list is provided in a related post on this site https://tambonthongchai.com/2018/06/25/a-greenhouse-effect-of-atmospheric-co2/ .

(9) Climate science has responded to the uncertainty issue in various ways by using fudge factors such as the amount of ocean heat content change needed to smooth out and explain observational anomalies. Other climate science responses have included a biased interpretation of uncertainty by viewing uncertainty as a confidence interval and then altering the probability of the confidence interval placing a greater emphasis on the high end of the interval. Related post: https://tambonthongchai.com/2020/04/22/climate-science-uncertainty/

(10) In spite of these efforts, Uncertainty in the ECS grew into a contentious issue in climate science and nowhere more egregious than in the long range forecasts of future temperature by the IPCC that are used to motivate and construct climate action plans in terms reductions in fossil fuel emissions. The ECS uncertainty issue in climate science severely weakened the rationale for the principal policy implication of climate change research, that of reducing fossil fuel emissions to avoid catastrophic runaway warming by way of GHG forcing. This crisis in climate science created an urgent research agenda to resolve this core issue in the policy implications of climate change.

PART-2: TCRE: TRANSIENT CLIMATE RESPONSE TO CUMULATIVE EMISSIONS

(11): A breakthrough came in 2009 when three papers were published with an alternative to the failed ECS parameter that connects emissions to warming. All three of them proposed the same new causal connection between emissions and warming.  These papers, the Matthews paper in particular, created a sensation in climate science as the community of scientists heaved a sigh of relief having re-established empirical evidence of human cause, crucial to the primary agenda of climate change research – that of reducing fossil fuel emissions and thereby moving the energy infrastructure away from fossil fuels. Here we present the paper by Damon Matthews.

(12) In 2009, Damon Matthews (and co-authors) submitted a paper to the journal Nature with an amazing discovery that could rescue climate science from the climate sensitivity uncertainty problem. He found a near perfect linear relationship and a near perfect correlation between cumulative fossil fuel emissions and surface temperature and proposed that the OLS linear regression coefficient of this line, expressed in Celsius units of warming per TTC (teratonnes of carbon equivalent) of cumulative emissions serves as an alternate measure of the global warming effect of emissions. This new and statistically stable rationale for attenuating warming by cutting fossil fuel emissions is thus proposed as a replacement for the failed ECS.

(13) The coefficient was named Transient Climate Response to Cumulative Emissions and was christened with the acronym TCRE and it quickly came to be accepted in climate science as a replacement for the failed ECS parameter.The TCRE became a sensational success. In accepting the (Matthews, 2009) paper for publication the editor of Nature wrote a congratulatory editorial comment as follows: “To date, efforts to describe and predict the climate response to human CO2 emissions have focused on climate sensitivity: the equilibrium temperature change associated with a doubling of CO2. But recent research has suggested that this ‘Charney’ sensitivity may be an incomplete representation of the full Earth system response, as it ignores changes in the carbon cycle, aerosols, land use and land cover. He continued: Matthews et al. propose a new measure, the transient climate response, or TCR. Using a combination of a simplified climate model, a range of simulations from a recent model inter-comparison, and historical constraints, they find that independent of the timing of emissions or the atmospheric concentration of CO2 emitting a trillion tonnes of carbon will cause 1.0 – 2.1 C of global warming, a TCRE value that is consistent with model predictions.

(14): The success of the TCRE as a foundational concept in relating climate change to emissions and thereby in developing carbon emission budgets for 2ºC and 1.5ºC warming targets led to its adoption by the IPCC in its AR5 report and also in its emergency call to the 1.5ºC carbon budget in the SR15 report of 2018. The initial value of 90%CI TCRE = [1.0 – 2.1]ºC per teratonne of cumulative emissions reported by Matthews was found to be very stable with later works reporting values within a narrow range of TCRE = [0.75-2.5] quite unlike the large range of ECS values listed in a related post [The Greenhouse Effect of Atmospheric CO2].

(15): The achievement was heralded by climate scientists around the world. A 2017 paper by converted ECS advocate Reto Knutti calls for discarding the ECS altogether and replacing it with the strong and stable TCRE as the primary climate science connection between emissions and warming. This connection is crucial to the anti fossil fuel activism function of AGW (Knutti 2017). In the Knutti 2017 paper titled “Beyond Equilibrium Climate Sensitivity”, he wrote “Estimates of the transient climate response are better constrained by observed warming and are more relevant for predicting warming over the next decades. Newer metrics relating global warming directly to the total emitted CO₂ show that in order to keep warming to within 2 °C, future CO₂ emissions have to remain strongly limited, irrespective of climate sensitivity being at the high or low end”.

(16): A crucial and attractive feature of the TCRE noted by Knutti and others is the relative ease and clarity with which the TCR can be translated into “carbon budgets” for any target amount of warming. A carbon budget is the amount of fossil fuel emissions that can be released to stay within a target amount of warming. For example, if TCR=2, then the carbon budget to keep warming within 2C is 1.0 teratonne and for a 1.5C target, the carbon budget is 0.75 teratonne, and so on. Thus, once the TCR was adopted by climate science as the new theory of human cause, climate action plans were presented in terms of carbon budgets constructed with the TCRE parameter.

(17): The success of the TCRE in terms of empirical verification and its practical application in the construction of carbon budgets notwithstanding, the construction of the TCRE contains a fatal statistical flaw that renders the TCRE a spurious statistic with no interpretation in terms of phenomena in the real world it apparently represents. These statistical issues are described in related posts: LINK#1: https://tambonthongchai.com/2018/05/06/tcre/ LINK#2: https://tambonthongchai.com/2018/12/03/tcruparody/

(18): The other issue is that the move from ECS to TCR created a vacuum in AGW theory. Whereas the ECS was based on the theory that atmospheric CO2 concentration is responsive to fossil fuel emissions and the theory that surface temperature is responsive to atmospheric CO2 concentration, the TCR is a purely empirical construct with no theoretical understanding of the climate science mechanism by which cumulative emissions drive surface temperature.

(19): The disconnect between climate change theory and the TCRE becomes apparent in terms of the mathematical inconsistencies described in a related post: LINK: https://tambonthongchai.com/2020/08/26/a-mathematical-inconsistency/ . The disconnection between theory and method creates confirmation bias in the use of the TCRE tool to rationalize observations in terms of flexible earth system models as described below. LINK to post on Confirmation Bias: https://tambonthongchai.com/2018/08/03/confirmationbias/

(20): This theoretical vacuum created a confirmation bias opportunity to explain any level of TCR warming in terms of any combination of a large number of known climate drivers and their positive and negative feedback mechanisms without the need for an exclusive focus on the GHG effect of atmospheric CO2 concentration in terms of a climate sensitivity parameter that had created the unresolved uncertainty issue in the original theory of AGW.

(21): Once the TCR warming forecast is accepted, climate science can then go through the data for all biogeochemical processes that can interact with the physical climate and their assumed interaction with the GHG effect of CO2 to explain the observed value of the TCR. These biogeochemical processes can include but are not limited to (1) carbon cycle flows, (2) the deep ocean beyond the “slab” surface layer concept, (3) the cooling effect of aerosols particularly sulfate aerosols, the various and poorly understood complex effect of clouds, and the impact of other anthropogenic chemicals in atmospheric composition such as ozone. These climate drivers, though not understood well enough to predict warming can nevertheless be interpreted in terms of the TCR warming in a confirmation bias logic that has become accepted procedure in climate science.

(22): The analysis begins with the TCR warming and then searches for known climate drivers that would explain the observed TCR. This unlimited extension of AGW theory is then incorporated into a new generation of climate models called ESM or Earth System Models. Unlike the older climate models that begin with emissions and predict warming with climate sensitivity, ESM analysis begins with emissions and the TCR warming and then searches for all possible explanations for the TCR warming in terms of known climate drivers. Although impressive in its expansive assessment of global warming, the procedure is a creation of confirmation bias and the statistical issues described in PART-3 below.

PART-3: STATISTICAL ISSUES IN THE TCR AND ESM. In the rush to the TCRE and to the carbon budgets constructed from the TCRE, and thereby to ESM climate models based on the TCRE as the starting point, climate science has overlooked some fundamental statistical issues. Surprisingly, these issues were ignored even after troublesome contradictions appeared in the use of the TCRE as the theory of AGW and the tool for the construction of carbon budgets in the climate action priority of AGW science.

One such issue apparently still unresolved in climate science is the remaining carbon budget. Briefly, the issue is that midway into a carbon budget period, the remaining carbon budget cannot be computed by subtraction. The unresolved struggle of climate science with the remaining carbon budget is highlighted in the bibliography below and also in related posts on this site where two of the papers listed in the bibliography are discussed. These are the Chris Jones and Pierre Friedlingstein 2020 paper https://tambonthongchai.com/2020/04/09/climate-statistics/ , and the Rogelj and Forster 2019 paper. https://tambonthongchai.com/2019/07/28/rcb/

What we find in these two papers is that the remaining carbon budget computed by subtraction of the emissions already made and that made with TCR computation for the remaining period do not match. This mismatch is then reconciled by the needed adjustments to the large suite of climate forcings and feedbacks in the ESM for the two time spans involved – a case of the same kind of confirmation bias and the Texas Sharpshooter fallacy that is used to construct the ESM explanation for the TCR . A detailed response to the Chris Jones and Pierre Friedlingstein 2020 paper points out the statistical flaws in the interpretation of the remaining carbon budget anomaly in terms of the ESM suite of convenience. LINK: https://tambonthongchai.com/2020/04/09/climate-statistics/ . An analysis of the remaining carbon budget issue is provided in a related post: LINK: https://tambonthongchai.com/2019/12/25/the-remaining-carbon-budget-anomaly-explained/

At the root of these complexities and the confused state of the ESM science of the TCR is that the TCR is based on a spurious correlation as explained in a related post LINK: https://tambonthongchai.com/2018/05/06/tcre/ .

As explained in the related post linked above, a time series of the cumulative values of another time series contains neither time scale nor degrees of freedom and therefore it does not contain useful information. The observed correlation derives from the sign pattern of the data in which emissions are always positive and in a period of warming, the annual warming is mostly positive. Under these conditions, the time series of cumulative values of annual emissions and annual warming will tend to be correlated although without time scale and without degrees of freedom. When the sign pattern is removed and randomized, the correlation vanishes.

The remaining carbon budget anomaly can be understood in this context. LINK: https://tambonthongchai.com/2019/12/25/the-remaining-carbon-budget-anomaly-explained/ . Since the annual warming that are positive is random, it is unlikely that the fraction of positive warming years in the two sub-periods will be the same.

Thus the only information content of strong correlations between cumulative values of time series data is that they happen to follow certain sign patterns. The further interpretation of these correlations and regression coefficients in terms of human cause of warming and in terms of carbon budgets is not possible. The attempt to interpret this correlation and its TCR regression coefficient in terms of climate change with the confirmation bias exercise of selecting ESM forcings to match the observed TCR does not yield useful information.

(23): A detailed response to the Chris Jones and Pierre Friedlingstein 2020 paper on TCRE carbon budgets provides further support for this conclusion. LINK: https://tambonthongchai.com/2020/04/09/climate-statistics/ . The summary of that review is reproduced below:

(24): ABSTRACT: Climate science has misinterpreted anomalies created by statistical errors as a climate science issue that needs to be resolved with climate models of greater complexity. In this context we find that their struggle with the remaining carbon budget puzzle demonstrates a failure of climate science to address statistical issues of the TCRE. This failure has led them down a complex and confusing path of trying to find a climate science explanation of the remaining carbon budget anomaly that was created by statistical errors. The research paper presented below serves as an example of this kind of climate research. The real solution to the remaining carbon budget puzzle is to understand the statistical flaws in the TCRE correlation described in the two linked documents and to stop using it [LINK] [LINK] . To drive home the point of the spuriousness of the TCRE correlation we show in the second linked document that the TCRE procedure that shows that fossil fuel emissions cause warming also shows that UFOs cause warming [LINK] .  

RELATED POSTS ON TCRE CARBON BUDGETS

1. ILLUSORY CARBON BUDGETS: https://tambonthongchai.com/2019/08/02/illusorytcre/
2. CLIMATE SCIENCE VERSUS STATISTICS: https://tambonthongchai.com/2019/07/28/rcb/
3. GRETA THUNBERG’S CARBON BUDGET: https://tambonthongchai.com/2020/01/02/greta-thunbergs-carbon-budget/
4. THE REMAINING CARBON BUDGET EXPLAINED: https://tambonthongchai.com/2019/12/25/the-remaining-carbon-budget-anomaly-explained/
5. THE REMAINING CARBON BUDGET ISSUE: https://tambonthongchai.com/2019/11/08/remainingcarbonbudget/
6. THE TCR CARBON BUDGETS OF CLIMATE SCIENCE: https://tambonthongchai.com/?s=carbon+budget
7. THE TCR CARBON BUDGET CONUNDRUM: https://tambonthongchai.com/2019/08/16/carbonbudgetconundrum/
8. CARBON BUDGETS AND THE TCR: https://tambonthongchai.com/2019/08/06/tcrebudget/
9. ILLUSORY CARBON BUDGETS: https://tambonthongchai.com/2019/08/02/illusorytcre/
10. CARBON BUDGETS AND CLIMATE MITIGATION PATHWAYS: https://tambonthongchai.com/2019/01/14/carbonbudget/

(25): Yet another issue with the TCRE as a replacement of the ECS and the use of the TCRE to construct carbon budgets for a warming mechanism described in terms of the ECS is that the interpretation of ECS warming with the TCRE contains a fatal mathematical inconsistency as described in a related post: LINK: https://tambonthongchai.com/2020/08/26/a-mathematical-inconsistency/ . Briefly, “The ECS measure of the impact of fossil fuel emissions on warming holds that atmospheric CO2 concentration at any given time is a linear function of cumulative emissions and that surface temperature is a logarithmic function of atmospheric CO2 concentration. These two relationships imply that surface temperature is a logarithmic function of cumulative emissions. That in turn implies that the amount of warming caused by a given level of cumulative emissions is the difference between the logarithms of the two cumulative emissions before and after. The TCR measure of the impact of fossil fuel emissions on warming holds that the amount of warming is a linear function of cumulative emissions. This linearity is mathematically inconsistent with the ECS measure which implies that the amount of warming is proportional to the difference between the logarithms of the cumulative emissions before and after the period of warming under study. The mathematical inconsistency implies that the significant research effort in climate science to resolve the ECS and TCR measures of anthropogenic warming with Earth System Models {ESM} is not possible because the two methods of computing the impact of emissions on temperature are not mathematically consistent.

PART-4: THE RELEVANT BIBLIOGRAPHY

1. The utility of the historical record for assessing the transient climate response to cumulative emissions
   Richard J. Millar and Pierre Friedlingstein
   Published:02 April 2018: https://doi.org/10.1098/rsta.2016.0449Abstract: The historical observational record offers a way to constrain the relationship between cumulative carbon dioxide emissions and global mean warming. We use a standard detection and attribution technique, along with observational uncertainties to estimate the all-forcing or ‘effective’ transient climate response to cumulative emissions (TCRE) from the observational record. Accounting for observational uncertainty and uncertainty in historical non-CO2 radiative forcing gives a best-estimate from the historical record of 1.84°C/TtC (1.43–2.37°C/TtC 5–95% uncertainty) for the effective TCRE and 1.31°C/TtC (0.88–2.60°C/TtC 5–95% uncertainty) for the CO2-only TCRE. While the best-estimate TCRE lies in the lower half of the IPCC likely range, the high upper bound is associated with the not-ruled-out possibility of a strongly negative aerosol forcing. Earth System Models have a higher effective TCRE range when compared like-for-like with the observations over the historical period, associated in part with a slight underestimate of diagnosed cumulative emissions relative to the observational best-estimate, a larger ensemble mean-simulated CO2-induced warming, and rapid post-2000 non-CO2 warming in some ensemble members. This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels’. FULL TEXT https://royalsocietypublishing.org/doi/10.1098/rsta.2016.0449
2. Jones, Chris D., and Pierre Friedlingstein. “Quantifying process-level uncertainty contributions to TCRE and Carbon Budgets for meeting Paris Agreement climate targets.” Environmental Research Letters (2020). To achieve the goals of the Paris Agreement requires deep and rapid reductions in anthropogenic CO₂ emissions, but uncertainty surrounds the magnitude and depth of reductions. Earth system models provide a means to quantify the link from emissions to global climate change. Using the concept of TCRE—the transient climate response to cumulative carbon emissions—we can estimate the remaining carbon budget to achieve 1.5 or 2 °C. But the uncertainty is large, and this hinders the usefulness of the concept. Uncertainty in carbon budgets associated with a given global temperature rise is determined by the physical Earth system, and therefore Earth system modelling has a clear and high priority remit to address and reduce this uncertainty. Here we explore multi-model carbon cycle simulations across three generations of Earth system models to quantitatively assess the sources of uncertainty which propagate through to TCRE. Our analysis brings new insights which will allow us to determine how we can better direct our research priorities in order to reduce this uncertainty. We emphasise that uses of carbon budget estimates must bear in mind the uncertainty stemming from the biogeophysical Earth system, and we recommend specific areas where the carbon cycle research community needs to re-focus activity in order to try to reduce this uncertainty. We conclude that we should revise focus from the climate feedback on the carbon cycle to place more emphasis on CO₂ as the main driver of carbon sinks and their long-term behaviour. Our proposed framework will enable multiple constraints on components of the carbon cycle to propagate to constraints on remaining carbon budgets. FULL TEXT: https://iopscience.iop.org/article/10.1088/1748-9326/ab858a/meta
3. Ehlert, Dana, et al. “The sensitivity of the proportionality between temperature change and cumulative CO2 emissions to ocean mixing.” Journal of Climate 30.8 (2017): 2921-2935. The ratio of global mean surface air temperature change to cumulative CO₂ emissions, referred to as transient climate response to cumulative CO₂ emissions (TCRE), has been shown to be approximately constant on centennial time scales. The mechanisms behind this constancy are not well understood, but previous studies suggest that compensating effects of ocean heat and carbon fluxes, which are governed by the same ocean mixing processes, could be one cause for this approximate constancy. This hypothesis is investigated by forcing different versions of the University of Victoria Earth System Climate Model, which differ in the ocean mixing parameterization, with an idealized scenario of 1% annually increasing atmospheric CO₂ until quadrupling of the preindustrial CO₂ concentration and constant concentration thereafter. The relationship between surface air warming and cumulative emissions remains close to linear, but the TCRE varies between model versions, spanning the range of 1.2°–2.1°C EgC⁻¹ at the time of CO₂ doubling. For all model versions, the TCRE is not constant over time while atmospheric CO₂ concentrations increase. It is constant after atmospheric CO₂ stabilizes at 1120 ppm, because of compensating changes in temperature sensitivity (temperature change per unit radiative forcing) and cumulative airborne fraction. The TCRE remains approximately constant over time even if temperature sensitivity, determined by ocean heat flux, and cumulative airborne fraction, determined by ocean carbon flux, are taken from different model versions with different ocean mixing settings. This can partially be explained with temperature sensitivity and cumulative airborne fraction following similar trajectories, which suggests ocean heat and carbon fluxes scale approximately linearly with changes in vertical mixing. FULL TEXT: https://journals.ametsoc.org/jcli/article/30/8/2921/95100
4. Rogelj, Joeri, et al. “Estimating and tracking the remaining carbon budget for stringent climate targets.” Nature 571.7765 (2019): 335-342. Research reported during the past decade has shown that global warming is roughly proportional to the total amount of carbon dioxide released into the atmosphere. This makes it possible to estimate the remaining carbon budget: the total amount of anthropogenic carbon dioxide that can still be emitted into the atmosphere while holding the global average temperature increase to the limit set by the Paris Agreement. However, a wide range of estimates for the remaining carbon budget has been reported, reducing the effectiveness of the remaining carbon budget as a means of setting emission reduction targets that are consistent with the Paris Agreement. Here we present a framework that enables us to track estimates of the remaining carbon budget and to understand how these estimates can improve over time as scientific knowledge advances. We propose that application of this framework may help to reconcile differences between estimates of the remaining carbon budget and may provide a basis for reducing uncertainty in the range of future estimates. FULL TEXT https://www.nature.com/articles/s41586-019-1368-z
5. Tokarska, Katarzyna B., and Nathan P. Gillett. “Cumulative carbon emissions budgets consistent with 1.5 C global warming.” Nature Climate Change 8.4 (2018): 296-299. The Paris Agreement¹ commits ratifying parties to pursue efforts to limit the global temperature increase to 1.5 °C relative to pre-industrial levels. Carbon budgets^2,3,4,5 consistent with remaining below 1.5 °C warming, reported in the IPCC Fifth Assessment Report (AR5)^2,6,8, are directly based on Earth system model (Coupled Model Intercomparison Project Phase 5)⁷ responses, which, on average, warm more than observations in response to historical CO₂ emissions and other forcings^8,9. These models indicate a median remaining budget of 55 PgC (ref. ¹⁰, base period: year 1870) left to emit from January 2016, the equivalent to approximately five years of emissions at the 2015 rate^11,12. Here we calculate warming and carbon budgets relative to the decade 2006–2015, which eliminates model–observation differences in the climate–carbon response over the historical period⁹, and increases the median remaining carbon budget to 208 PgC (33–66% range of 130–255 PgC) from January 2016 (with mean warming of 0.89 °C for 2006–2015 relative to 1861–1880^{13,14,15,16,17,18}). There is little sensitivity to the observational data set used to infer warming that has occurred, and no significant dependence on the choice of emissions scenario. Thus, although limiting median projected global warming to below 1.5 °C is undoubtedly challenging^19,20,21, our results indicate it is not impossible, as might be inferred from the IPCC AR5 carbon budgets.
6. Mengis, Nadine, and H. Damon Matthews. “Non-CO 2 forcing changes will likely decrease the remaining carbon budget for 1.5° C.” npj Climate and Atmospheric Science 3.1 (2020): 1-7. One key contribution to the wide range of 1.5 °C carbon budgets among recent studies is the non-CO₂ climate forcing scenario uncertainty. Based on a partitioning of historical non-CO₂ forcing, we show that currently there is a net negative non-CO₂ forcing from fossil fuel combustion (FFC), and a net positive non-CO₂ climate forcing from land-use change (LUC) and agricultural activities. We perform a set of future simulations in which we prescribed a 1.5 °C temperature stabilisation trajectory, and diagnosed the resulting 1.5 °C carbon budgets. Using the historical partitioning, we then prescribed adjusted non-CO₂ forcing scenarios consistent with our model’s simulated decrease in FFC CO₂ emissions. We compared the diagnosed carbon budgets from these adjusted scenarios to those resulting from the default RCP scenario’s non-CO₂ forcing, and to a scenario in which proportionality between future CO₂ and non-CO₂ forcing is assumed. We find a wide range of carbon budget estimates across scenarios, with the largest budget emerging from the scenario with assumed proportionality of CO₂ and non-CO₂ forcing. Furthermore, our adjusted-RCP scenarios produce carbon budgets that are smaller than the corresponding default RCP scenarios. Our results suggest that ambitious mitigation scenarios will likely be characterised by an increasing contribution of non-CO₂ forcing, and that an assumption of continued proportionality between CO₂ and non-CO₂ forcing would lead to an overestimate of the remaining carbon budget. Maintaining such proportionality under ambitious fossil fuel mitigation would require mitigation of non-CO₂ emissions at a rate that is substantially faster than found in the standard RCP scenarios. FULL TEXT https://www.nature.com/articles/s41612-020-0123-3
7. Jones, Chris, et al. “Twenty-first-century compatible CO2 emissions and airborne fraction simulated by CMIP5 earth system models under four representative concentration pathways.” Journal of Climate 26.13 (2013): 4398-4413. The carbon cycle is a crucial Earth system component affecting climate and atmospheric composition. The response of natural carbon uptake to CO₂ and climate change will determine anthropogenic emissions compatible with a target CO₂ pathway. For phase 5 of the Coupled Model Intercomparison Project (CMIP5), four future representative concentration pathways (RCPs) have been generated by integrated assessment models (IAMs) and used as scenarios by state-of-the-art climate models, enabling quantification of compatible carbon emissions for the four scenarios by complex, process-based models. Here, the authors present results from 15 such Earth system GCMs for future changes in land and ocean carbon storage and the implications for anthropogenic emissions. The results are consistent with the underlying scenarios but show substantial model spread. Uncertainty in land carbon uptake due to differences among models is comparable with the spread across scenarios. Model estimates of historical fossil-fuel emissions agree well with reconstructions, and future projections for representative concentration pathway 2.6 (RCP2.6) and RCP4.5 are consistent with the IAMs. For high-end scenarios (RCP6.0 and RCP8.5), GCMs simulate smaller compatible emissions than the IAMs, indicating a larger climate–carbon cycle feedback in the GCMs in these scenarios. For the RCP2.6 mitigation scenario, an average reduction of 50% in emissions by 2050 from 1990 levels is required but with very large model spread (14%–96%). The models also disagree on both the requirement for sustained negative emissions to achieve the RCP2.6 CO₂ concentration and the success of this scenario to restrict global warming below 2°C. All models agree that the future airborne fraction depends strongly on the emissions profile with higher airborne fraction for higher emissions scenarios. FULL TEXT https://journals.ametsoc.org/jcli/article/26/13/4398/34280
8. Anav, A., et al. “Evaluating the land and ocean components of the global carbon cycle in the CMIP5 earth system models.” Journal of Climate 26.18 (2013): 6801-6843. The authors assess the ability of 18 Earth system models to simulate the land and ocean carbon cycle for the present climate. These models will be used in the next Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) for climate projections, and such evaluation allows identification of the strengths and weaknesses of individual coupled carbon–climate models as well as identification of systematic biases of the models. Results show that models correctly reproduce the main climatic variables controlling the spatial and temporal characteristics of the carbon cycle. The seasonal evolution of the variables under examination is well captured. However, weaknesses appear when reproducing specific fields: in particular, considering the land carbon cycle, a general overestimation of photosynthesis and leaf area index is found for most of the models, while the ocean evaluation shows that quite a few models underestimate the primary production.The authors also propose climate and carbon cycle performance metrics in order to assess whether there is a set of consistently better models for reproducing the carbon cycle. Averaged seasonal cycles and probability density functions (PDFs) calculated from model simulations are compared with the corresponding seasonal cycles and PDFs from different observed datasets. Although the metrics used in this study allow identification of some models as better or worse than the average, the ranking of this study is partially subjective because of the choice of the variables under examination and also can be sensitive to the choice of reference data. In addition, it was found that the model performances show significant regional variations. FULL TEXT https://journals.ametsoc.org/jcli/article/26/18/6801/34409
9. Stevens, Bjorn, and Sandrine Bony. “What are climate models missing?.” Science 340.6136 (2013): 1053-1054. Fifty years ago, Joseph Smagorinsky published a landmark paper (1) describing numerical experiments using the primitive equations (a set of fluid equations that describe global atmospheric flows). In so doing, he introduced what later became known as a General Circulation Model (GCM). GCMs have come to provide a compelling framework for coupling the atmospheric circulation to a great variety of processes. Although early GCMs could only consider a small subset of these processes, it was widely appreciated that a more comprehensive treatment was necessary to adequately represent the drivers of the circulation. But how comprehensive this treatment must be was unclear and, as Smagorinsky realized (2), could only be determined through numerical experimentation. These types of experiments have since shown that an adequate description of basic processes like cloud formation, moist convection, and mixing is what climate models miss most. FULL TEXT https://science.sciencemag.org/content/340/6136/1053.full
10. Anderson, Thomas R., Ed Hawkins, and Philip D. Jones. “CO2, the greenhouse effect and global warming: from the pioneering work of Arrhenius and Callendar to today’s Earth System Models.” Endeavour 40.3 (2016): 178-187. Climate warming during the course of the twenty-first century is projected to be between 1.0 and 3.7 °C depending on future greenhouse gas emissions, based on the ensemble-mean results of state-of-the-art Earth System Models (ESMs). Just how reliable are these projections, given the complexity of the climate system? The early history of climate research provides insight into the understanding and science needed to answer this question. We examine the mathematical quantifications of planetary energy budget developed by Svante Arrhenius (1859–1927) and Guy Stewart Callendar (1898–1964) and construct an empirical approximation of the latter, which we show to be successful at retrospectively predicting global warming over the course of the twentieth century. This approximation is then used to calculate warming in response to increasing atmospheric greenhouse gases during the twenty-first century, projecting a temperature increase at the lower bound of results generated by an ensemble of ESMs (as presented in the latest assessment by the Intergovernmental Panel on Climate Change). This result can be interpreted as follows. The climate system is conceptually complex but has at its heart the physical laws of radiative transfer. This basic, or “core” physics is relatively straightforward to compute mathematically, as exemplified by Callendar’s calculations, leading to quantitatively robust projections of baseline warming. The ESMs include not only the physical core but also climate feedbacks that introduce uncertainty into the projections in terms of magnitude, but not sign: positive (amplification of warming). As such, the projections of end-of-century global warming by ESMs are fundamentally trustworthy: quantitatively robust baseline warming based on the well-understood physics of radiative transfer, with extra warming due to climate feedbacks. These projections thus provide a compelling case that global climate will continue to undergo significant warming in response to ongoing emissions of CO₂ and other greenhouse gases to the atmosphere. FULL TEXT https://www.sciencedirect.com/science/article/pii/S0160932716300308
11. Bentsen, Mats, et al. “The Norwegian earth system model, NorESM1-M-Part 1: Description and basic evaluation.” GMDD 5.3 (2012): 2843-2931. The core version of the Norwegian Climate Center’s Earth System Model, named NorESM1-M, is presented. The NorESM-family of models are based on the Community Climate System Model version 4 (CCSM4) of the University Corporation for Atmospheric Research, but differs from the latter by, in particular, an isopycnic coordinate ocean model and advanced chemistry-aerosol-cloud-radiation interaction schemes. NorESM1-M has a horizontal resolution of approximately 2° for the atmosphere and land components and 1° for the ocean and ice components. NorESM is also available in a lower resolution version (NorESM1-L) and a version that includes prognostic biogeochemical cycling (NorESM1-ME). The latter two model configurations are not part of this paper. Here, a first-order assessment of the model stability, the mean model state and the internal variability based on the model experiments made available to CMIP5 are presented. Further analysis of the model performance is provided in an accompanying paper (Iversen et al., 2012), presenting the corresponding climate response and scenario projections made with NorESM1-M. FULL TEXT PDF https://gmd.copernicus.org/preprints/5/2843/2012/gmdd-5-2843-2012.pdf
12. McMullin, Barry, et al. “Assessing negative carbon dioxide emissions from the perspective of a national “fair share” of the remaining global carbon budget.” Mitigation and Adaptation Strategies for Global Change (2019): 1-24. We present an assessment of the plausible Paris-aligned fair share net cumulative carbon dioxide (CO₂) quota for an example nation state, the Republic of Ireland. By Paris-aligned, we mean consistent with the Paris Agreement adopted at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change, at Paris, France, in December 2015 (UNFCCC 2015). We compare and contrast this quota with both the aspirations expressed in the current Irish National Policy Position and current national emission projections. The fair share quota is assessed as a maximum of c. 391 million tonnes of carbon dioxide (MtCO₂), equal to 83 tonnes of carbon dioxide (tCO₂) per capita, from 2015, based on a precautionary estimate of the global carbon budget (GCB) and specific interpretation of global equity. Given Ireland’s high current CO₂ per capita emission rate, this would correspond to sustained year-on-year reductions in nett annual CO₂ emissions of over − 11% per year (beginning as of 2016). By contrast, the CO₂ mitigation target indicated in the National Policy Position corresponds to nett annual reduction rates in the range of only −4.7% per year (low ambition) up to a maximum of − 8.3% per year (high ambition), and projections based on current and immediately planned mitigation measures indicate the possibility, instead, of sustained increases in emissions at a rate of the order of + 0.7% per year. Accordingly, there is a large gap between Paris-aligned ambition and current political and policy reality on the ground, with a significant risk of early emergence of “CO₂ debt” and tacit reliance on rapid deployment of currently speculative (at a relevant scale and feasible cost) negative CO₂ emission technologies to actively remove CO₂ from the atmosphere. While the detailed policy situation will clearly differ from country to country, we suggest that this methodology, and its CO₂debt framing, may be usefully applied in other individual countries or regions. We recommend that such framing be incorporated explicitly into a global mitigation strategy via the statements of nationally determined contributions required to be submitted and updated by all parties under the Paris Agreement processes.
13. Flato, Gregory M. “Earth system models: an overview.” Wiley Interdisciplinary Reviews: Climate Change 2.6 (2011): 783-800. Earth System models (ESMs) are global climate models with the added capability to explicitly represent biogeochemical processes that interact with the physical climate and so alter its response to forcing such as that associated with human‐caused emissions of greenhouse gases. Representing the global carbon cycle allows for feedbacks between the physical climate and the biological and chemical processes in the ocean and on land that take up some of the emitted carbon dioxide and so act to reduce warming. The sulfur cycle is also important in that both natural and human emissions of sulfur contribute to the production of sulfate aerosols which reflect incoming solar radiation (a direct cooling effect) and alter cloud properties (an indirect cooling effect). Other components such as ozone are also being incorporated into some ESMs. Evaluating the physical component of an ESM is becoming increasingly comprehensive and sophisticated, but the evaluation of the biogeochemical components suffer somewhat from a lack of comprehensive global‐scale observational data. Nevertheless, such models provide valuable insight into climate variability and change, and the role of human activities and possible mitigation actions on future climate change. Internationally coordinated experiments are increasingly important in providing a multimodel ensemble of climate simulations, thereby taking advantage of some ‘cancellation of errors’ and allowing better quantification of uncertainty. WIREs Clim Change 2011, 2:783–800. doi: 10.1002/wcc.148 FULL TEXT PDF http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.662.1789&rep=rep1&type=pdf
14. McPhaden, Michael J., Stephen E. Zebiak, and Michael H. Glantz. “ENSO as an integrating concept in earth science.” science 314.5806 (2006): 1740-1745. The El Niño–Southern Oscillation (ENSO) cycle of alternating warm El Niño and cold La Niña events is the dominant year-to-year climate signal on Earth. ENSO originates in the tropical Pacific through interactions between the ocean and the atmosphere, but its environmental and socioeconomic impacts are felt worldwide. Spurred on by the powerful 1997–1998 El Niño, efforts to understand the causes and consequences of ENSO have greatly expanded in the past few years. These efforts reveal the breadth of ENSO’s influence on the Earth system and the potential to exploit its predictability for societal benefit. However, many intertwined issues regarding ENSO dynamics, impacts, forecasting, and applications remain unresolved. Research to address these issues will not only lead to progress across a broad range of scientific disciplines but also provide an opportunity to educate the public and policy makers about the importance of climate variability and change in the modern world. FULL TEXT https://science.sciencemag.org/content/314/5806/1740.full
15. Clark, Martyn P., et al. “Improving the representation of hydrologic processes in Earth System Models.” Water Resources Research 51.8 (2015): 5929-5956. Many of the scientific and societal challenges in understanding and preparing for global environmental change rest upon our ability to understand and predict the water cycle change at large river basin, continent, and global scales. However, current large‐scale land models (as a component of Earth System Models, or ESMs) do not yet reflect the best hydrologic process understanding or utilize the large amount of hydrologic observations for model testing. This paper discusses the opportunities and key challenges to improve hydrologic process representations and benchmarking in ESM land models, suggesting that (1) land model development can benefit from recent advances in hydrology, both through incorporating key processes (e.g., groundwater‐surface water interactions) and new approaches to describe multiscale spatial variability and hydrologic connectivity; (2) accelerating model advances requires comprehensive hydrologic benchmarking in order to systematically evaluate competing alternatives, understand model weaknesses, and prioritize model development needs, and (3) stronger collaboration is needed between the hydrology and ESM modeling communities, both through greater engagement of hydrologists in ESM land model development, and through rigorous evaluation of ESM hydrology performance in research watersheds or Critical Zone Observatories. Such coordinated efforts in advancing hydrology in ESMs have the potential to substantially impact energy, carbon, and nutrient cycle prediction capabilities through the fundamental role hydrologic processes play in regulating these cycles. FULL TEXT https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015WR017096%4010.1002/%28ISSN%291944-7973.WRR50
17. Fisher, Rosie A., et al. “Vegetation demographics in Earth System Models: A review of progress and priorities.” Global change biology 24.1 (2018): 35-54. Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics in ESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real‐world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first‐generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter‐disciplinary communication. FULL TEXT PDF https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcb.13910
18. Getzlaff, Julia, and Heiner Dietze. “Effects of increased isopycnal diffusivity mimicking the unresolved equatorial intermediate current system in an earth system climate model.” Geophysical Research Letters 40.10 (2013): 2166-2170. Earth system climate models generally underestimate dissolved oxygen concentrations in the deep eastern equatorial Pacific. This problem is associated with the “nutrient trapping” problem, described by Najjar et al. [1992], and is, at least partially, caused by a deficient representation of the Equatorial Intermediate Current System (EICS). Here we emulate the unresolved EICS in the UVic earth system climate model by locally increasing the zonal isopycnal diffusivity. An anisotropic diffusivity of ∼50,000 m² s⁻¹ yields an improved global representation of temperature, salinity and oxygen. In addition, it (1) resolves most of the local “nutrient trapping” and associated oxygen deficit in the eastern equatorial Pacific and (2) reduces spurious zonal temperature gradients on isopycnals without affecting other physical metrics such as meridional overturning or air‐sea heat fluxes. Finally, climate projections of low‐oxygenated waters and associated denitrification change sign and apparently become more plausible. FULL TEXT https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/grl.50419
20. Pielke Sr, Roger A., et al. “Unresolved issues with the assessment of multidecadal global land surface temperature trends.” Journal of Geophysical Research: Atmospheres 112.D24 (2007). This paper documents various unresolved issues in using surface temperature trends as a metric for assessing global and regional climate change. A series of examples ranging from errors caused by temperature measurements at a monitoring station to the undocumented biases in the regionally and globally averaged time series are provided. The issues are poorly understood or documented and relate to micrometeorological impacts due to warm bias in nighttime minimum temperatures, poor siting of the instrumentation, effect of winds as well as surface atmospheric water vapor content on temperature trends, the quantification of uncertainties in the homogenization of surface temperature data, and the influence of land use/land cover (LULC) change on surface temperature trends. Because of the issues presented in this paper related to the analysis of multidecadal surface temperature we recommend that greater, more complete documentation and quantification of these issues be required for all observation stations that are intended to be used in such assessments. This is necessary for confidence in the actual observations of surface temperature variability and long‐term trends. FULL TEXT https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2006JD008229