Multi-model simulations for climate prediction

New paper alert! A fully coupled climate reanalysis by Vince Cooper covering 1850-2023. We used strongly coupled data assimilation on observations of sea surface temperature, land-based air temperature, sea-level pressure over the ocean, and satellite sea-ice concentration at monthly resolution. As far as we know, this is the first time that these fields have been simultaneously reconstructed over the historical period. Results show significant low-frequency variance in ENSO, with a peak near the start of the 20th century, muted modern cooling trends in Southern Ocean SST (see figure below), a decline in Arctic sea-ice area since the 19th century, and relatively small changes in Antarctic sea-ice area. Additional key points: * Most reanalysis datasets consider each component of the climate system independently (i.e., separate atmospheric and oceanic reanalyses), leading to inconsistencies in coupled variability. Here, we use strongly coupled data assimilation, which means that all observations update every component of the climate system. * Efficient emulators are used to propagate the memory of past observations forward in time. We use cyclostationary linear inverse models trained on 8 CMIP6 model simulations to include the role of model error in the reconstructions. These models are used to create 8 separate reanalyses, propagating the full error covariance matrix for all climate variables. * A 1600-member ensemble is created by sampling the posterior distributions in a dynamically consistent process, providing a large sample of equally likely reanalyses of historical climate. This provides a rich dataset for exploring climate variability with uncertainty quantification. The preprint can be found here: https://lnkd.in/gbEtR4Jw

Scientists Combine Climate Models For More Accurate Projections -- https://lnkd.in/ga-82Kaw <-- shared technical article -- https://lnkd.in/gHFTDAYj <-- shared paper -- Researchers... have created a new method for statistically analyzing climate models that projects future conditions with more fidelity. The method provides a way to adjust for models with high temperature sensitivities—a known problem in the community. By assigning different weights to models and combining them, the researchers estimate that the global temperature will increase between 2 and 5° Celsius by the end of the century. This projection, published in Nature Communications Earth & Environment [link above], aligns with previous projections, although this novel framework is more inclusive, avoiding the rejection of models that was common practice in previous methods... A key parameter for these models—known as equilibrium climate sensitivity or ECS—describes the relationship between change in carbon dioxide and corresponding warming. Although the Earth system has a true ECS, it is not a measurable quantity. Different lines of evidence can provide a plausible picture of the Earth's true ECS, which can alleviate the uncertainty of simulation models. However, many models assume a high ECS and predict higher temperatures in response to more atmospheric carbon dioxide than occurs in the real Earth system. Because these models provide estimates about future conditions to scientists and policymakers, it is important to ensure that they represent the conditions of the Earth as faithfully as possible. Previous methods mitigated this issue by eliminating models with a high ECS value. "That was a heavy-handed approach," said Massoud. "The models that were thrown out might have good information that we need, especially for understanding the extreme ends of things." "Instead, we adopted a tool called Bayesian Model Averaging, which is a way to combine models with varying influence when estimating their distribution," said Massoud. "We used this to constrain the ECS on these models, which enabled us to project future conditions without the 'hot model problem.'"... This new method provides a framework for how to best understand a collection of climate models. The model weights included in this research informed the Fifth National Climate Assessment, a report released on Nov. 14 that gauges the impacts of climate change in the United States. This project also supports the Earth System Grid Federation, an international collaboration led in the U.S. by DOE that manages and provides access to climate models and observed data…” #GIS #spatial #mapping #climatechange #spatialanalysis #spatiotemporal #model #modeling #numericmodeling #global #statistics #weighting #bayesian #modelaverging #climatesensivity #climatemodels #projection #ECS #earthsystem #ORNL

“Recent publication of a new nationally-consistent set of river flow and groundwater level projections based on state-of-the-art UKCP18 climate projections offers a unique opportunity to quantitatively assess future UK hydrological drought susceptibility”   The dataset includes a transient, multi-model ensemble of hydrological projections driven by a single regional climate model (RCM) for 200 catchments and 54 boreholes spanning a period from 1961 to 2080.   Assessment of a baseline period (1989–2018) shows that the RCM-driven projections adequately reproduce observed river flow and groundwater level regimes, improving our confidence in using these models for assessment of future drought.     Across all hydrological models and most catchments, future low river flows are projected to decline consistently out to 2080.  Drought durations, intensities and severities are all projected to increase in most UK catchments.   However, the trajectory of low groundwater levels and groundwater drought characteristics diverge from those of river flows.   Whilst groundwater levels at most boreholes are projected to decline (consistent with river flows), the majority of boreholes show <10 % reduction in transient low groundwater levels by 2080 and eight show moderate increases.   See Parry et al. (2023) in HESS, “Divergent future drought projections in UK river flows and groundwater levels”