Assessing Prior Emergent Constraints on Surface Albedo Feedback in CMIP6

An emergent constraint (EC) is a popular model evaluation technique, which offers the potential to reduce intermodel variability in projections of climate change. Two examples have previously been laid out for future surface albedo feedbacks (SAF) stemming from loss of Northern Hemisphere (NH) snow cover (SAFsnow) and sea ice (SAFice). These processes also have a modern-day analog that occurs each year as snow and sea ice retreat from their seasonal maxima, which is strongly correlated with future SAF across an ensemble of climate models. The newly released CMIP6 ensemble offers the chance to test prior constraints through out-of-sample verification, an important examination of EC robustness. Here, we show that the SAFsnow EC is equally strong in CMIP6 as it was in past generations, while the SAFice EC is also shown to exist in CMIP6, but with different, slightly weaker characteristics. We find that the CMIP6 mean NH SAF exhibits a global feedback of 0.25 ± 0.05 W m−2 K−1, or ~61% of the total global albedo feedback, largely in line with prior generations despite its increased climate sensitivity. The NH SAF can be broken down into similar contributions from snow and sea ice over the twenty-first century in CMIP6. Crucially, intermodel variability in seasonal SAFsnow and SAFice is largely unchanged from CMIP5 because of poor outlier simulations of snow cover, surface albedo, and sea ice thickness. These outliers act to mask the noted improvement from many models when it comes to SAFice, and to a lesser extent SAFsnow.

Underestimation of the projected 21st century increase in drought duration, according to emergent constraint: consistent result in CMIP5 and CMIP6 models

<p>Droughts are defined as one of the most devastating natural disasters of modern times and a key challenge faced under climate change. The complexity of interacting physical processes that underlie the shortage of rainfall in climate models hampers accurate representation of present-day droughts, and leads to differences in their responses to increased greenhouse gas (GHG) concentrations in the future. As a result, the confidence in drought projections is currently defined as ‘medium to low' by the Intergovernmental Panel on Climate Change (IPCC), and reducing this uncertainty remains one of the main goals in coming years, with significant benefits for human and natural systems. </p><p>In this study we explore a relationship between biases in simulated present-day values of longest annual drought (LAD) and future projections of LAD in an ensemble of CMIP5 and CMIP6 models. We find that present-day model bias explains almost 95 % of the future uncertainty in LAD by the end of the 21st century, attributed to the well-known precipitation simulation errors: “drier” models with longer annual droughts at present tend to predict larger LAD values worldwide in the future, as well as a stronger response to GHG forcing in LAD, which is significant in more than 40 % of the global land area.</p><p>Substituting observational LAD estimates from satellite data into this model-revealed “present–future relationlarship” suggests that the 21st century global mean increase in duration of annual meteorological droughts could be significantly larger than predicted by the CMIP5 and CMIP6 model ensembles. This emergent constraint reduces global mean uncertainty range in future LAD estimates from 45–100 to 75–90 days, a level more typical of the prediction range of “drier” models. The findings reveal world regions where climate change may cause stronger meteorological drought aggravation than expected, and emphasise the importance of reducing model errors, which are presently largely owed to rain biases, to increase confidence in future predictions.</p>

Arctic Ocean acidification over the 21st century co-driven by anthropogenic carbon increases and freshening in the CMIP6 model ensemble

<p>The uptake of anthropogenic carbon (C<sub>ant</sub>) by the ocean leads to ocean acidification, causing the reduction of pH and the calcium carbonate saturation states of aragonite (Ω<sub>arag</sub>) and calcite (Ω<sub>calc</sub>). The Arctic Ocean is particularly vulnerable to ocean acidification due to its naturally low pH and saturation states and due to ongoing freshening and the concurrent reduction in alkalinity in this region. Here, we present projections of  C<sub>ant</sub> and ocean acidification in the Arctic Ocean over the 21<sup>st</sup> century across Earth System Models (ESMs) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared to the previous model generation (CMIP5), the inter-model uncertainty of projected end-of-century Arctic Ocean Ω<sub>arag/calc</sub> is reduced by 44–64 %. The strong reduction in projection uncertainties of Ω<sub>arag/calc</sub> can be attributed to compensation between C<sub>ant</sub> uptake and alkalinity reduction in the latest models. Specifically, ESMs with a large increase in Arctic Ocean C<sub>ant</sub> over the 21<sup>st</sup> century tend to simulate a relatively weak concurrent freshening and alkalinity reduction, while ESMs with a small increase in C<sub>ant</sub> simulate a relatively strong freshening and concurrent alkalinity reduction. Although both mechanisms contribute to Arctic Ocean acidification over the 21<sup>st</sup> century, the increase in C<sub>ant</sub> remains the dominant driver. Even under the low-emissions shared socioeconomic pathway SSP1-2.6, basin-wide averaged aragonite undersaturation occurs before the end of the century. While under the high-emissions pathway SSP5-8.5, the Arctic Ocean mesopelagic is projected to even become undersaturated with respect to calcite. An emergent constraint, identified in CMIP5, which relates present-day maximum sea surface densities in the Arctic Ocean to the projected end-of-century Arctic Ocean C<sub>ant</sub> inventory, is found to generally hold in CMIP6. However, a coincident constraint on Arctic declines in Ω<sub>arag/calc</sub> is not apparent in the new generation of models. This is due to both the reduction in Ω<sub>arag/calc</sub> projection uncertainty and the weaker direct relationship between projected changes in Arctic Ocean C<sub>ant</sub> and Ω<sub>arag/calc</sub>. In CMIP6, models generally better simulate maximum sea surface densities in the Arctic Ocean and consequently the transport of C<sub>ant</sub> into the Arctic Ocean interior, with simulated historical increases in C<sub>ant</sub> in improved agreement with observational products.</p>

Constraining Amazonian land surface temperature sensitivity to precipitation and the probability of forest dieback

The complete or partial collapse of the forests of Amazonia is consistently named as one of the top ten possible tipping points of Planet Earth in a changing climate. However, apart from a few observational studies that showed increased mortality after the severe droughts of 2005 and 2010, the evidence for such collapse depends primarily on modelling. Such studies are notoriously deficient at predicting the rainfall in the Amazon basin and how the vegetation interacts with the rainfall is poorly represented. Here, we use long-term surface-based observations of the air temperature and rainfall in Amazonia to provide a constraint on the modelled sensitivity of temperature to changes in precipitation. This emergent constraint also allows us to significantly constrain the likelihood of a forest collapse or dieback. We conclude that Amazon dieback under IPCC scenario RCP8.5 (crossing the tipping point) is not likely to occur in the twenty-first century.

Future changes in the frequency of temperature extremes may be underestimated in tropical and subtropical regions

In a warming world, temperature extremes are expected to show a distinguishable change over much of the globe even at 1.5 °C warming, and in many regions this change has already been detected in observations. Although many studies predict an increase in heat extreme events, the magnitude of the change varies greatly among different models even for the same mean warming. This uncertainty has been linked to differences in land–atmosphere feedback across models. Here we show that a significant constraint for future projections can be based on the ability of climate models to accurately simulate the present day variability of daily surface maximum temperature. An emergent constraint on Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6) models, applied to ERA5 reanalysis, indicates that the best estimate in hot extreme changes by the end of the century could be worse than previously estimated, mostly for tropical and subtropical regions as well as South and East Asia.

Emergent constraints on climate—carbon cycle feedbacks from tropical atmospheric aridity

The vulnerability of the terrestrial tropical carbon cycle to changes in climate, especially temperature and moisture, remains one of the largest sources of uncertainty in future climate projections. Harnessing new satellite-driven global carbon reanalysis, we show here that tropical atmospheric aridity, which is directly related to the atmospheric vapor pressure deficit (VPD), is a causal driver of the interannual variability of the tropical net carbon balance and consequently the CO2 growth rate with observed present-day sensitivities of -3.2 ± 0.62 GtC mb-1 yr-1. Our results provide evidence that a large part of tropical net biome exchange variability is indirectly driven by land-atmospheric coupling via VPD variations that cannot be explained by tropical temperatures alone. Furthermore, we find that there is an emergent relationship between the sensitivity of the tropical carbon balance to VPD and the long-term response of tropical-land carbon storage to increase in VPD across an ensemble of Earth System Models used in the Climate Model Intercomparison Project 6 (CMIP6). Employing a hierarchical emergent constraint, the global carbon—climate feedback from aridity is -22±11 GtC mb-1 which represents a substantial reduction in uncertainty relative to the CMIP6 ensemble. Our findings show that atmospheric aridity is an important proxy for the combined effects of both water and temperature on the terrestrial carbon balance and a key predictor of carbon—climate feedbacks.

Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?

An important metric for temperature projections is the equilibrium climate sensitivity (ECS), which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which are the major source of spread in ECS among current models. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here have been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a decrease in skill and statistical significance of the emergent relationship for nearly all constraints, with this decrease being large in many cases. Consequently, the size of the constrained ECS ranges (66 % confidence intervals) widens by 51 % on average in CMIP6 compared to CMIP5. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble that the emergent constraint was originally derived from. The emergently- constrained best estimates of ECS also increased from CMIP5 to CMIP6 by 12 % on average. This can be at least partly explained by the increased number of high-ECS (above 4.5 K) models in CMIP6 without a corresponding change in the constraint predictors, suggesting the emergence of new feedback processes rather than changes in strength of those previously dominant. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that process-based emergent constraints on ECS should rather be thought of as constraints for the process or feedback they are actually targeting.

Arctic Ocean acidification over the 21^st century co-driven by anthropogenic carbon increases and freshening in the CMIP6 model ensemble

The uptake of anthropogenic carbon (Cant) by the ocean leads to ocean acidification, causing the reduction of pH and the calcium carbonate saturation states of aragonite (Ωarag) and calcite (Ωcalc). The Arctic Ocean is particularly vulnerable to ocean acidification due to its naturally low pH and saturation states and due to ongoing freshening and the concurrent reduction in alkalinity in this region. Here, we analyse ocean acidification in the Arctic Ocean over the 21st century across 14 Earth System Models (ESMs) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared to the previous model generation (CMIP5), the inter-model uncertainty of projected end-of-century Arctic Ocean Ωarag/calc is reduced by 44–64 %. The strong reduction in projection uncertainties of Ωarag/calc can be attributed to compensation between Cant uptake and alkalinity reduction in the latest models. Specifically, ESMs with a large increase in Arctic Ocean Cant over the 21st century tend to simulate a relatively weak concurrent freshening and alkalinity reduction, while ESMs with a small increase in Cant simulate a relatively strong freshening and concurrent alkalinity reduction. Although both mechanisms contribute to Arctic Ocean acidification over the 21st century, the increase in Cant remains the dominant driver. Even under the low-emissions shared socioeconomic pathway SSP1-2.6, basin-wide averaged arag undersaturation occurs before the end of the century. While under the high-emissions pathway SSP5-8.5, the Arctic Ocean mesopelagic is projected to even become undersaturated with respect to calcite. An emergent constraint, identified in CMIP5, which relates present-day maximum sea surface densities in the Arctic Ocean to the projected end-of-century Arctic Ocean Cant inventory, is found to generally hold in CMIP6. However, a coincident constraint on Arctic declines in Ωarag/calc is not apparent in the new generation of models. This is due to both the reduction in Ωarag/calc projection uncertainty and the weaker direct relationship between projected changes in Arctic Ocean Cant and arag/calc. In CMIP6, models generally better simulate maximum sea surface densities in the Arctic Ocean and consequently the transport of Cant into the Arctic Ocean interior, with simulated historical increases in Cant in improved agreement with observational products.

A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming

Carbon cycle feedbacks represent large uncertainties in climate change projections, and the response of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil carbon depend on changes in litter and root inputs from plants and especially on reductions in the turnover time of soil carbon (τs) with warming. An approximation to the latter term for the top one metre of soil (ΔCs,τ) can be diagnosed from projections made with the CMIP6 and CMIP5 Earth System Models (ESMs), and is found to span a large range even at 2 °C of global warming (−196 ± 117 PgC). Here, we present a constraint on ΔCs,τ, which makes use of current heterotrophic respiration and the spatial variability of τs inferred from observations. This spatial emergent constraint allows us to halve the uncertainty in ΔCs,τ at 2 °C to −232 ± 52 PgC.