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The historical global temperature record is an essential data product for quantifying the variability and change of the Earth system. In recent years, better characterization of observational uncertainty in global and hemispheric trends has become available, but the methodologies are not necessarily applicable to analyses at smaller regional areas, or monthly or seasonal means, where station sparsity and other systematic issues contribute to greater uncertainty. This study presents a gridded uncertainty ensemble of historical surface temperature anomalies from the Goddard Institute for Space Studies (GISS) Surface Temperature (GISTEMP) product. This ensemble characterizes the complex spatial and temporal correlation structure of uncertainty, enabling better uncertainty propagation for climate and applied science in applications of historical temperature products at spatial scales from global to regional and temporal scales from centennial to monthly. This work details the methodology for generating the uncertainty ensemble, presents key statistics of the uncertainty evolution over space and time, and provides best practices for using the uncertainty ensemble in future studies. Summary statistics from the uncertainty ensemble agree well with the previous GISTEMP global uncertainty assessment, providing confidence in both. Plain Language Summary: NASA's historical surface temperature product, GISTEMP, combines measurements of sea surface temperature and land surface air temperature to estimate how temperature has changed on Earth since 1880. In this study, we combine all known sources of uncertainty in these measurements to determine the best estimate as well as the likely interval for surface temperature in the global average as well as on a latitude‐longitude grid. Results agree with previous uncertainty assessments of GISTEMP as well as uncertainty assessments of other major global temperature products. The uncertainty is quantified through an uncertainty ensemble, or 200 equally likely reconstructions of monthly mean surface temperature. This uncertainty ensemble makes it easy to incorporate the uncertainty in surface temperature in subsequent studies. Key Points: A 200‐member ensemble has been created to quantify uncertainty in the GISTEMP history of surface temperature anomaliesThis ensemble enables more accurate statistical analyses of key global change metricsThe median ensemble estimate agrees well with the operational GISTEMP analysis and other global products [ABSTRACT FROM AUTHOR]

Palaeoclimate variations are an essential component in constraining future projections of climate change as a function of increasing anthropogenic greenhouse gases. The Earth System Sensitivity (ESS) describes the multi-millennial response of Earth (in terms of global mean temperature) to a doubling of CO2 concentrations. A recent study used a correlation of inferred temperatures and radiative forcing from greenhouse gases over the past 800,000 years to estimate the ESS from present day CO2 is about 9°C, and to imply a long-term commitment of 3–7°C even if greenhouse gas levels remain at present-day concentrations. However, we demonstrate that the methodology of ref. does not reliably estimate the ESS in the presence of orbital forcing of ice age cycles and therefore conclude that the inferred present-day committed warming is considerably overestimated. There is a Reply to this Comment by Snyder, C. W. Nature 547, http://dx.doi.org/10.1038/nature22804 (2017).

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long‐term adaptation strategies, and guide mitigation approaches. Increasingly complex socio‐economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high‐resolution modeling of global‐to‐local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science‐to‐service pathways, and co‐production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally‐funded U.S. modeling groups outline here perspectives on a six‐pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services. Plain Language Summary: In the face of a changing climate, the understanding, predictions and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long‐term adaptation strategies, and guide mitigation approaches. Increasingly complex socio‐economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities currently at hand, and working with stakeholders to co‐produce the information that they require. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally‐funded U.S. modeling groups outline here perspectives on a six‐pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support the next generation of climate services. Key Points: Facing the uncharted territory of a changed climate and increasingly complex socio‐economic systems requires the best possible predictionsScientific and technological opportunities are at hand to accelerate progress on U.S. climate predictions and projectionsCapitalizing on the most transformative opportunities calls for an unprecedented level of coordination and resources [ABSTRACT FROM AUTHOR]

Trawling the seafloor can disturb carbon that took millennia to accumulate, but the fate of that carbon and its impact on climate and ecosystems remains unknown. Using satellite-inferred fishing events and carbon cycle models, we find that 55-60% of trawling-induced aqueous CO2 is released to the atmosphere over 7-9 years. Using recent estimates of bottom trawling's impact on sedimentary carbon, we found that between 1996-2020 trawling could have released, at the global scale, up to 0.34-0.37 Pg CO2 yr-1 to the atmosphere, and locally altered water pH in some semi-enclosed and heavy trawled seas. Our results suggest that the management of bottom-trawling efforts could be an important climate solution. [ABSTRACT FROM AUTHOR]

A 10-member ensemble simulation with the NASA GISS-E2-1-G climate model shows a clear bifurcation in the Atlantic meridional overturning circulation (AMOC) strength under the SSP2–4.5 extended scenario. At 26°N, the bifurcation leads to 8 strong AMOC and 2 much weaker AMOC states, while at 48°N, it leads to 8 stable AMOC-on and 2 nearly AMOC-off states, the latter lasting approximately 800 years. A variety of fully coupled models have demonstrated tipping points in AMOC through hosing experiments, i.e., prescribing sufficient freshwater inputs in the subpolar North Atlantic. In the GISS simulations, there are no external freshwater perturbations. The bifurcation arises freely in the coupled system and is the result of stochastic variability (noise-induced bifurcation) associated with sea ice transport and melting in the Irminger Sea after a slowing of the greenhouse gas forcing. While the AMOC strength follows the near shutdown of the Labrador Sea deep convection initially, the Irminger Sea salinity and deep mixing determine the timing of the AMOC recovery or near collapse at 48°N, which varies widely across the ensemble members. Other feedbacks such as ice-albedo, ice-evaporation, E − P, and the overturning salt-advection feedback play a secondary role that may enhance or reduce the primary mechanism which is ice melt. We believe this is the first time that a coupled climate model has shown such a bifurcation across an initial condition ensemble and might imply that there is a chance for significant and prolonged AMOC slow down due to internal variability of the system. Significance Statement: We believe this is the first time that divergent AMOC behavior has been reported for an ensemble of Earth system model simulations using identical climate forcing and no prescribed freshwater perturbations. This response is a manifestation of noise-induced bifurcation, enhanced by feedbacks, revealing the role stochastic (or intrinsic) variability may play in AMOC stability. [ABSTRACT FROM AUTHOR]

Snowstorms cause substantial disruption in the eastern United States and Canada each winter. While reductions in annual snowfall are projected over most of this region due to anthropogenic global warming, daily snowfall extremes that have the greatest impact may not decrease in the same manner. We examine changes to two extreme snowfall metrics: the 95th percentile of daily snowfall (SF95, cm) and the number of events during which 10% of the mean annual snowfall is exceeded during a single day (TC10, events yr−1). We explore changes to these metrics in two ensembles of the fifth‐generation Canadian Regional Climate Model, including four 0.22° (≈25 km) simulations driven by different coupled general circulation models as well as the higher‐resolution (0.11°, ≈12 km) ClimEx ensemble, driven by 50 members of a large initial‐condition ensemble of one global model. We find that while mean annual snowfall is projected to decrease over our domain, SF95 is projected to remain relatively constant, suggesting that the most extreme daily snowfalls currently observed are likely to occur even in a warmer future climate. The region of the largest TC10 values exhibits a northward shift, with a larger percentage of annual snowfall occurring during a few large events along the U.S.‐Canada border. These projected changes to the nature of snowfall events may have important socioeconomic consequences in this densely populated region of North America. Plain Language Summary: Snowstorms affect the highly populated regions of the northeastern United States and southeastern Canada every winter, disrupting ground and air travel and resulting in substantial socioeconomic impacts. Understanding how climate change may impact snowstorms is necessary for this region to prepare for the future. We explore how snowstorms in these regions may change in a future, warmer world using two sets of climate model simulations. We find that yearly total snowfall is likely to decrease over most of this region, with the largest declines to the south and smaller declines farther north. Despite these decreases, we find that the snowfall amounts that currently occur during the largest snowstorms are still likely to occur even in a much warmer future climate. Finally, we examine snowstorms that produce a large percentage of the annual snowfall during a single event. We find that the region where these big snowstorms relative to the yearly snowfall occur most often will shift northward in the future. In summary, while yearly snowfall is likely to decrease nearly everywhere in eastern North America, significant snowstorms will continue to occur, and some regions will see more of their yearly snowfall during a few large events. Key Points: Mean annual snowfall is projected to decrease across most of eastern North America as the Earth continues to warmThe most intense high‐impact snowfall events currently observed will continue to occur in a warmer climateA larger percentage of yearly snowfall is likely to fall during a few large events near the Canada‐United States border [ABSTRACT FROM AUTHOR]

Observational studies have shown that the El Niño–Southern Oscillation (ENSO) exerts an influence on the Quasi-Biennial Oscillation (QBO). The downward propagation of the QBO tends to speed up and slow down during El Niño and La Niña, respectively. Recent results from general circulation models have indicated that the ENSO modulation of the QBO requires a relatively high horizontal resolution, and that it does not show up in the climate models with parameterized but temporally constant gravity wave sources. Here, we demonstrate that the NASA GISS E2.2 models can capture the observed ENSO modulation of the QBO period with a horizontal resolution of 2° latitude by 2.5° longitude and its gravity wave sources parameterized interactively. This is because El Niño events lead to more vigorous gravity wave sources generating more absolute momentum fluxes over the equatorial belt, as well as less filtering of these waves into the tropical lower stratosphere through a weakening of the Walker circulation. Various components of the ENSO system such as the SSTs, the convective activities, and the Walker circulation are intimately involved in the generation and propagation of parameterized gravity waves, through which ENSO modulates the QBO period in GISS E2.2 models. [ABSTRACT FROM AUTHOR]