The Intergovernmental Panel on Climate Change (IPCC), to its credit, has recognized this 'hot model' problem. Scientists contributing to the main sections of its Sixth Assessment Report (AR6; ...published over the past few months) reconciled the newest climate models with key observational constraints on global mean warming, sea-level rise and ocean heat content, and other analyses. ...some studies have reported projections that might be inconsistent with AR6 assessments. Hot tail The largest source of uncertainty in global temperatures 50 or 100 years from now is the volume of future greenhouse-gas emissions, which are largely under human control.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Retrospectively comparing future model projections to observations provides a robust and independent test of model skill. Here we analyze the performance of climate models published between 1970 and ...2007 in projecting future global mean surface temperature (GMST) changes. Models are compared to observations based on both the change in GMST over time and the change in GMST over the change in external forcing. The latter approach accounts for mismatches in model forcings, a potential source of error in model projections independent of the accuracy of model physics. We find that climate models published over the past five decades were skillful in predicting subsequent GMST changes, with most models examined showing warming consistent with observations, particularly when mismatches between model‐projected and observationally estimated forcings were taken into account.
Plain Language Summary
Climate models provide an important way to understand future changes in the Earth's climate. In this paper we undertake a thorough evaluation of the performance of various climate models published between the early 1970s and the late 2000s. Specifically, we look at how well models project global warming in the years after they were published by comparing them to observed temperature changes. Model projections rely on two things to accurately match observations: accurate modeling of climate physics and accurate assumptions around future emissions of CO2 and other factors affecting the climate. The best physics‐based model will still be inaccurate if it is driven by future changes in emissions that differ from reality. To account for this, we look at how the relationship between temperature and atmospheric CO2 (and other climate drivers) differs between models and observations. We find that climate models published over the past five decades were generally quite accurate in predicting global warming in the years after publication, particularly when accounting for differences between modeled and actual changes in atmospheric CO2 and other climate drivers. This research should help resolve public confusion around the performance of past climate modeling efforts and increases our confidence that models are accurately projecting global warming.
Key Points
Evaluation of uninitialized multidecadal climate model future projection performance provides a concrete test of model skill
The quasi‐linear relationship between model/observed forcings and temperature change is used to control for errors in projected forcing
Model simulations published between 1970 and 2007 were skillful in projecting future global mean surface warming
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Improvements in the GISTEMP Uncertainty Model Lenssen, Nathan J. L.; Schmidt, Gavin A.; Hansen, James E. ...
Journal of geophysical research. Atmospheres,
27 June 2019, Volume:
124, Issue:
12
Journal Article
Peer reviewed
Open access
We outline a new and improved uncertainty analysis for the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature ...anomalies are derived from historical weather station data and ocean data from ships, buoys, and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses, and incorporate independently derived estimates for ocean data processing, station homogenization, and other structural biases. The resulting 95% uncertainties are near 0.05 °C in the global annual mean for the last 50 years and increase going back further in time reaching 0.15 °C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date) and conclude with 86% likelihood that 2016 was indeed the hottest year of the instrumental period (so far).
Key Points
A total uncertainty analysis for GISTEMP is presented for the first time
Uncertainty in global mean surface temperature is roughly 0.05 degrees Celsius in recent decades increasing to 0.15 degrees Celsius in the nineteenth century
Annual mean uncertainties are small relative to the long‐term trend
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
We present a new 3‐dimensional 1° × 1° gridded data set for the annual mean seawater oxygen isotope ratio (δ18O) to use in oceanographic and paleoceanographic applications. It is constructed from a ...large set of observations made over the last 50 years combined with estimates from regional δ18O to salinity relationships in areas of sparse data. We use ocean fronts and water mass tracer concentrations to help define distinct water masses over which consistent local relationships are valid. The resulting data set compares well to the GEOSECS data (where available); however, in certain regions, particularly where sea ice is present, significant seasonality may bias the results. As an example application of this data set, we use the resulting surface δ18O as a boundary condition for isotope‐enabled GISS ModelE to yield a more realistic comparison to the isotopic composition of precipitation data, thus quantifying the ‘source effect’ of δ18O on the isotopic composition of precipitation.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Southern Ocean surface cooling and Antarctic sea ice expansion from 1979 through 2015 have been linked both to changing atmospheric circulation and melting of Antarctica's grounded ice and ice ...shelves. However, climate models have largely been unable to reproduce this behavior. Here we examine the contribution of observed wind variability and Antarctic meltwater to Southern Ocean sea surface temperature (SST) and Antarctic sea ice. The free‐running, CMIP6‐class GISS‐E2.1‐G climate model can simulate regional cooling and neutral sea ice trends due to internal variability, but they are unlikely. Constraining the model to observed winds and meltwater fluxes from 1990 through 2021 gives SST variability and trends consistent with observations. Meltwater and winds contribute a similar amount to the SST trend, and winds contribute more to the sea ice trend than meltwater. However, while the constrained model captures much of the observed sea ice variability, it only partially captures the post‐2015 sea ice reduction.
Plain Language Summary
While most of the globe has warmed in recent decades, the Southern Ocean around Antarctica cooled at the surface and its sea ice expanded from the beginning of satellite observations in 1979 through 2015. This unexpected behavior has been linked to changes in winds and to the addition of cold, fresh water from the melting of Antarctic's ice sheet and ice shelves. However, the importance of these two potential drivers has been unclear, partly because global climate models have often struggled to reproduce the observed changes. Here, we modify a climate model, constraining it to simulate observed winds and adding in realistic amounts of meltwater. With these changes, the model can simulate changes in SST and sea ice that are similar to observations. Winds and meltwater both play an important role. However, they cannot fully explain the large Antarctic sea ice reductions that were observed after 2015, suggesting that other factors may be at play.
Key Points
We nudge winds to observations and add estimates of observed freshwater from ice sheet and ice shelf melt in a coupled climate model
Southern Ocean sea surface temperature trends and variability better match observations, with both winds and meltwater being important
The constrained model simulates strong Antarctic sea ice expansion and only partially captures recent sea ice lows
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Ample physical evidence shows that carbon dioxide (CO₂) is the single most important climate-relevant greenhouse gas in Earth's atmosphere. This is because CO₂, like ozone, N₂O, CH₄, and ...chlorofluorocarbons, does not condense and precipitate from the atmosphere at current climate temperatures, whereas water vapor can and does. Noncondensing greenhouse gases, which account for 25% of the total terrestrial greenhouse effect, thus serve to provide the stable temperature structure that sustains the current levels of atmospheric water vapor and clouds via feedback processes that account for the remaining 75% of the greenhouse effect. Without the radiative forcing supplied by CO₂ and the other noncondensing greenhouse gases, the terrestrial greenhouse would collapse, plunging the global climate into an icebound Earth state.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Scafetta (2022, https://doi.org/10.1029/2022gl097716) purports to test Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models through a comparison of temperature changes over three ...decades. Unfortunately, the paper contains numerous conceptual and statistical errors that undermine all of the conclusions. First, no uncertainty is given for the observational temperature difference, making it impossible to assess compatibility with any model result. Second, the CMIP6 data are the ensemble means for each model, but the metric being tested is sensitive to the internal variability and so the full ensemble for each model must be used. When this is corrected, the conclusion that “all models with ECS > 3.0°C overestimate the observed global surface warming” is not sustained. Third, the statistical test in Section 2 would reject all models even in a perfect model setup given sufficient ensemble members, thus the second conclusion “that spatial t-statistics rejects the data-model agreement” is also not sustainable.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The methods to quantify equilibrium climate sensitivity are still debated. We collect millennial length simulations of coupled climate models and show that the global mean equilibrium warming is ...higher than those obtained using extrapolation methods from shorter simulations. Specifically, 27 simulations with 15 climate models forced with a range of CO2 concentrations show a median 17% larger equilibrium warming than estimated from the first 150 years of the simulations. The spatial patterns of radiative feedbacks change continuously, in most regions reducing their tendency to stabilizing the climate. In the equatorial Pacific, however, feedbacks become more stabilizing with time. The global feedback evolution is initially dominated by the tropics, with eventual substantial contributions from the midlatitudes. Time dependent feedbacks underscore the need of a measure of climate sensitivity that accounts for the degree of equilibration, so that models, observations, and paleo proxies can be adequately compared and aggregated to estimate future warming.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
An emerging literature suggests that estimates of equilibrium climate sensitivity (ECS) derived from recent observations and energy balance models are biased low because models project more positive ...climate feedback in the far future. Here we use simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to show that across models, ECS inferred from the recent historical period (1979-2005) is indeed almost uniformly lower than that inferred from simulations subject to abrupt increases in CO2-radiative forcing. However, ECS inferred from simulations in which sea surface temperatures are prescribed according to observations is lower still. ECS inferred from simulations with prescribed sea surface temperatures is strongly linked to changes to tropical marine low clouds. However, feedbacks from these clouds are a weak constraint on long-term model ECS. One interpretation is that observations of recent climate changes constitute a poor direct proxy for long-term sensitivity.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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