Climate Model Simulations Research Articles

Inferences of water–ice cloud physics determined from MAVEN/IUVS aerosol retrievals and Mars GCM comparisons

We performed retrievals of dust and water–ice optical depths at select times spanning Mars years (MYs) 33 through 36. We used data taken by the Imaging Ultraviolet Spectrograph (IUVS) instrument aboard the Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft, whose precessing orbit allowed IUVS to image the diurnal evolution of water–ice clouds at local times throughout this MY range where they have not been studied. We created a radiative-transfer retrieval algorithm and used it to simultaneously fit the dust and water–ice optical depth of each usable spectrum in IUVS apoapse data for the majority of the MAVEN mission. We compared our results to other datasets to ensure our retrievals produced plausible values and discuss potential reasons for discrepancies. We obtained global climate model (GCM) simulations from two state-of-the-art models and compared our results to both sets of simulations. We show that there are significant differences between our water–ice retrievals and both sets of simulations, both in the spatial cloud patterns and column-integrated optical depths. We discuss the different approaches both models used for simulating these clouds and the implications for accurately modeling these clouds in future GCM work.

Climate variability, heat distribution, and polar amplification in the warm unipolar “icehouse” of the Oligocene

Abstract. The Oligocene (33.9–23.03 Ma) had warm climates with flattened meridional temperature gradients, while Antarctica retained a significant cryosphere. These may pose imperfect analogues to distant future climate states with unipolar icehouse conditions. Although local and regional climate and environmental reconstructions of Oligocene conditions are available, the community lacks synthesis of regional reconstructions. To provide a comprehensive overview of marine and terrestrial climate and environmental conditions in the Oligocene, and a reconstruction of trends through time, we review marine and terrestrial proxy records and compare these to numerical climate model simulations of the Oligocene. Results, based on the present relatively sparse data, suggest temperatures around the Equator that are similar to modern temperatures. Sea surface temperatures (SSTs) show patterns similar to land temperatures, with warm conditions at mid- and high latitudes (∼60–90°), especially in the Southern Hemisphere (SH). Vegetation-based precipitation reconstructions of the Oligocene suggest regionally drier conditions compared to modern times around the Equator. When compared to proxy data, climate model simulations overestimate Oligocene precipitation in most areas, particularly the tropics. Temperatures around the mid- to high latitudes are generally underestimated in models compared to proxy data and tend to overestimate the warming in the tropics. In line with previous proxy-to-model comparisons, we find that models underestimate polar amplification and overestimate the Equator-to-pole temperature gradient suggested from the available proxy data. This further stresses the urgency of solving this widely recorded problem for past warm climates, such as the Oligocene.

Tropical eastern Pacific cooling trend reinforced by human activity

It remains unresolved whether the La Niña-like sea surface temperature (SST) trend pattern during the satellite era, featuring a distinct warming in the northwest/southwest Pacific but cooling in the tropical eastern Pacific, is driven by either external forcing or internal variability. Here, by conducting a comprehensive analysis of observations and a series of climate model simulations for the historical period, we show that a combination of internal variability and human activity may have shaped the observed La Niña-like SST trend pattern. As in observations, SSTs in each model ensemble member show a distinct multi-decadal swing between El Niño-like and La Niña-like trend patterns due to internal variability. The ensemble-mean trends for some models are, however, found to exhibit an enhanced zonal SST gradient along the equatorial Pacific over periods such as 1979–2010, suggesting a role of external forcing. In line with this hypothesis, single-forcing large ensemble model simulations show that human-induced stratospheric ozone depletion and/or aerosol changes have acted to enhance the zonal SST gradient via strengthening of Pacific trade winds, although the effect is model dependent. Our finding suggests that the La Niña-like SST trend is unlikely to persist under sustained global warming because both the ozone and aerosol impacts will eventually weaken.

Pollen-based seasonal temperature reconstruction in Northeast China over the past 10,000 years, and its implications for understanding the Holocene Temperature Conundrum

The Holocene Temperature Conundrum refers to the mismatch between proxy-based temperature records and those based on climate model simulations. A possible reason for this mismatch is a putative proxy-based bias in reconstructed summer temperatures, and therefore, regional reconstructions of seasonal temperature are crucial for resolving the conundrum. In this paper, we reconstruct vegetation and climate changes over the last ∼10,000 years BP based on a high-resolution pollen record from Gushantun peatland, Changbai Mountains, Northeast China. Multiple quantitative reconstruction approaches were used and weighted averaging partial least squares regression (WAPLS) was found to be the most appropriate method for reconstructing Holocene temperature and precipitation. The reconstructed climate record shows that the Holocene Climate Optimum occurred between 8 ka and 6 ka and exhibited a cold month mean temperature that was 3 °C warmer than modern temperatures. Climate gradually cooled during late Holocene with a minimum cold month temperature of −19.6 °C. Four prominent cold events occurred around 8.7 ka BP, 7.8 ka BP, 5.7 ka BP, and 2.5 ka BP with an amplitude variation up to 3 °C. The synthesized seasonal temperature time series and a comparison with other proxies show that the decreasing trend in mean annual temperature is not a seasonal bias caused by summer temperature change. This study provides evidence of a Holocene seasonal temperature change at a regional scale and insights for further understanding of the Holocene Temperature Conundrum.

The Effects of Summer Snowfall on Arctic Sea Ice Radiative Forcing

AbstractSnow is the most reflective natural surface on Earth. Since fresh snow on bare sea ice increases the surface albedo, the impact of summer snow accumulation can have a negative radiative forcing effect, which would inhibit sea ice surface melt and potentially slow sea‐ice loss. However, it is not well known how often, where, and when summer snowfall events occur on Arctic sea ice. In this study, we used in situ and model snow depth data paired with surface albedo and atmospheric conditions from satellite retrievals to characterize summer snow accumulation on Arctic sea ice from 2003 to 2017. We found that, across the Arctic, ∼2 snow accumulation events occurred on initially snow‐free conditions each year. The average snow depth and albedo increases were ∼2 cm and 0.08, respectively. 16.5% of the snow accumulation events were optically thick (>3 cm deep) and lasted 2.9 days longer than the average snow accumulation event (3.4 days). Based on a simple, multiple scattering radiative transfer model, we estimated a −0.086 ± 0.020 W m−2 change in the annual average top‐of‐the‐atmosphere radiative forcing for summer snowfall events in 2003–2017. The following work provides new information on the frequency, distribution, and duration of observed snow accumulation events over Arctic sea ice in summer. Such results may be particularly useful in understanding the impacts of ephemeral summer weather on surface albedo and their propagating effects on the radiative forcing over Arctic sea ice, as well as assessing climate model simulations of summer atmosphere‐ice processes.

Amplified warming of North American cold extremes linked to human-induced changes in temperature variability

How global warming is impacting winter cold extremes is uncertain. Previous work has found decreasing winter temperature variability over North America which suggests a reduction in frequency and intensity of cold extremes relative to mean changes. However, others argue that cold air outbreaks are becoming more likely because of Arctic-induced changes in atmospheric circulation. Here we show that cold extremes over North America have warmed substantially faster than the winter mean temperature since 1980. This amplified warming is linked to both decreasing variance and changes in higher moments of the temperature distributions. Climate model simulations with historical forcings robustly capture the observed trends in extremes and variability. A pattern-based detection and attribution analysis shows that the changes in variability are detectable in observations and can be attributed to human influence. Our results highlight that human emissions are warming North American extreme cold temperatures beyond only shifting the winter mean temperature.

Deeper and stronger North Atlantic Gyre during the Last Glacial Maximum

Subtropical gyre (STG) depth and strength are controlled by wind stress curl and surface buoyancy forcing1,2. Modern hydrographic data reveal that the STG extends to a depth of about 1 km in the Northwest Atlantic, with its maximum depth defined by the base of the subtropical thermocline. Despite the likelihood of greater wind stress curl and surface buoyancy loss during the Last Glacial Maximum (LGM)3, previous work suggests minimal change in the depth of the glacial STG4. Here we show a sharp glacial water mass boundary between 33° N and 36° N extending down to between 2.0 and 2.5 km—approximately 1 km deeper than today. Our findings arise from benthic foraminiferal δ18O profiles from sediment cores in two depth transects at Cape Hatteras (36–39° N) and Blake Outer Ridge (29–34° N) in the Northwest Atlantic. This result suggests that the STG, including the Gulf Stream, was deeper and stronger during the LGM than at present, which we attribute to increased glacial wind stress curl, as supported by climate model simulations, as well as greater glacial production of denser subtropical mode waters (STMWs). Our data suggest (1) that subtropical waters probably contributed to the geochemical signature of what is conventionally identified as Glacial North Atlantic Intermediate Water (GNAIW)5–7 and (2) the STG helped sustain continued buoyancy loss, water mass conversion and northwards meridional heat transport (MHT) in the glacial North Atlantic.

A Potential Surface Warming Regime for Volcanic Super‐Eruptions Through Stratospheric Water Vapor Increases

AbstractLarge volcanic eruptions are known to influence the climate through a variety of mechanisms including aerosol‐forced cooling and warming via emitted CO2. The January 2022 Hunga shallow underwater eruption caused an increase in stratospheric water vapor, and demonstrated how the associated positive radiative forcing can be an important component of an eruption's climate forcing. We present interactive stratospheric aerosol model simulations of super‐volcanic eruptions with a range of SO2 emissions that can produce climate warming through feedback effects produced by a large igneous province (or “flood basalt”) mid‐latitude super‐eruption using Goddard Earth Observing System Chemistry Climate Model climate model simulations. The model experiments suggest total SO2 emissions ≳4,000 Tg/4 Gt generate a multi‐year period of sustained aerosol absorptive local‐heating of the upper troposphere and lower stratosphere and hence produce net climate warming after strong initial cooling. The eruptions produce stratospheric water vapor increases of factors of 8–600. The initiation of these feedbacks within the simulations suggest they could occur for individual stratovolcano eruptions of the scale of the Toba or Tambora eruptions. We note the sensitivity of our results to volcanic sulfate aerosol microphysics and model chemistry.

Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.

Decreasing global tropical cyclone frequency in CMIP6 historical simulations.

The impact of anthropogenic global warming on tropical cyclone (TC) frequency remains a challenging issue, partly due to a relatively short period of reliable observational TC records and inconsistencies in climate model simulations. Using TC detection from 20 CMIP6 historical simulations, we show that the majority (75%) of these models show a decrease in global-scale TC frequency from 1850 to 2014. We demonstrated that this result is largely explained by weakened mid-tropospheric upward motion in CMIP6 models over the Pacific and Atlantic main development regions. The reduced upward motion is due to a zonal circulation adjustment and shifts in Intertropical Convergence Zone in response to global warming. In the South Indian Ocean, reduced TC frequency is mainly due to the decreased survival rate of TC seeds because of an increased saturation deficit in a warming climate. Our analysis highlights global warming's potential impact on the historical decrease in global TC frequency.

Intercomparison of ocean temperature and circulation near the Galápagos islands in high-resolution models and observations

The mean structure and variability of the Equatorial Undercurrent (EUC) have important implications for upwelling, sea surface temperature (SST), and productivity in the ecologically vital Galápagos Cold Pool. Historically, global coupled climate model simulations have exhibited considerable biases in their simulation of the EUC due to the requirement of relatively high spatial resolution to represent its dynamics. Particularly in the eastern equatorial Pacific, models must also adequately resolve important topographic features to accurately simulate the regional circulation. Here, we examine the extent to which a high-resolution configuration of the NCAR Community Earth System Model (CESM) and a suite of models from the High-Resolution Model Intercomparison project (HighResMIP) adequately represent the regional ocean circulation and other important climatological features of the eastern equatorial Pacific such as the EUC and the associated temperature patterns defining the cold tongue/Galápagos Cold Pool complex. Comparisons with satellite SST and in situ velocity observations, and a high-resolution ocean reanalysis product, illustrate that the high-resolution configuration of the CESM captures many key aspects of the SST field and EUC uniquely well, including its seasonal-to-interannual variability in the eastern equatorial Pacific. Specific strengths and biases of this model with direct comparison to the HighResMIP ensemble are discussed in detail, along with the potential for application of these models to interdisciplinary research topics such as projecting climate change impacts on marine ecosystems.

Carbon Cycle Instability for High-CO2 Exoplanets: Implications for Habitability

Implicit in the definition of the classical circumstellar habitable zone (HZ) is the hypothesis that the carbonate-silicate cycle can maintain clement climates on exoplanets with land and surface water across a range of instellations by adjusting atmospheric CO2 partial pressure (pCO2). This hypothesis is made by analogy to the Earth system, but it is an open question whether silicate weathering can stabilize climate on planets in the outer reaches of the HZ, where instellations are lower than those received by even the Archean Earth and CO2 is thought likely to dominate atmospheres. Since weathering products are carried from land to ocean by the action of water, silicate weathering is intimately coupled to the hydrologic cycle, which intensifies with hotter temperatures under Earth-like conditions. Here, we use global climate model simulations to demonstrate that the hydrologic cycle responds counterintuitively to changes in climate on planets with CO2-H2O atmospheres at low instellations and high pCO2, with global evaporation and precipitation decreasing as pCO2 and temperatures increase at a given instellation. Within the Maher & Chamberlain (or MAC) weathering formulation, weathering then decreases with increasing pCO2 for a range of instellations and pCO2 typical of the outer reaches of the HZ, resulting in an unstable carbon cycle that may lead to either runaway CO2 accumulation or depletion of CO2 to colder (possibly snowball) conditions. While the behavior of the system has not been completely mapped out, the results suggest that silicate weathering could fail to maintain habitable conditions in the outer reaches of the nominal HZ.

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from Climate Models

Abstract Precipitation values produced by climate models are biased due to the parameterization of physical processes and limited spatial resolution. Current bias-correction approaches usually focus on correcting lower-order statistics (mean and standard deviation), which make it difficult to capture precipitation extremes. However, accurate modeling of extremes is critical for policymaking to mitigate and adapt to the effects of climate change. We develop a deep learning framework, leveraging information from key dynamical variables impacting precipitation to also match higher-order statistics (skewness and kurtosis) for the entire precipitation distribution, including extremes. The deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Using climate model simulation data and observations that are climatologically similar but not strictly paired, the UFNet identifies and corrects the climate model biases, significantly improving the estimation of daily precipitation as measured by a broad range of spatiotemporal statistics. In particular, UFNet significantly improves the underestimation of extreme precipitation values seen with current bias-correction methods. Our approach constitutes a generalized framework for correcting other climate model variables which improves the accuracy of the climate model predictions, while utilizing a simpler and more stable training process.

STAR‐ESDM: A Generalizable Approach to Generating High‐Resolution Climate Projections Through Signal Decomposition

AbstractHigh‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally.

How Volcanic Aerosols Globally Inhibit Precipitation

AbstractVolcanic aerosols reduce global mean precipitation in the years after major eruptions, yet the mechanisms that produce this response have not been rigorously identified. Volcanic aerosols alter the atmosphere's energy balance, with precipitation changes being one pathway by which the atmosphere acts to return toward equilibrium. By examining the atmosphere's energy budget in climate model simulations using radiative kernels, we explain the global precipitation reduction as largely a consequence of Earth's surface cooling in response to volcanic aerosols reflecting incoming sunlight. These aerosols also directly add energy to the atmosphere by absorbing outgoing longwave radiation, which is a major cause of precipitation decline in the first post‐eruption year. We additionally identify factors limiting the post‐eruption precipitation decline, and provide evidence that our results are robust across climate models.

Stronger Arctic amplification from anthropogenic aerosols than from greenhouse gases

Arctic amplification (AA), the greater Arctic surface warming compared to the global average, has been widely attributed to increasing concentrations of greenhouse gases (GHG). However, less is known about the impacts of other forcings - notably, anthropogenic aerosols (AER) - and how they may compare to the impacts of GHG. Here we analyze sets of climate model simulations, specifically designed to isolate the AER and GHG effects on global climate. Surprisingly, we find stronger AA produced by AER than by GHG during the 1955–1984 period, when the strongest global AER increase. This stronger AER-induced AA is due to a greater sensitivity of Arctic sea ice, and associated changes in ocean-to-atmosphere heat exchange, to AER forcing. Our findings highlight the asymmetric Arctic climate response to GHG and AER forcings, and show that clean air policies which have reduced aerosol emissions may have exacerbated the Arctic warming over the past few decades.

The role of the tropical Atlantic in tropical Pacific climate variability

Interactions between Atlantic and Pacific Oceans can affect tropical Pacific variability and its global impacts at both interannual and decadal timescales. Thus, a deepened understanding of the coupled Atlantic-Pacific interactions is needed. While possible dynamical mechanisms by which the Atlantic can influence the Pacific have been identified, the effectiveness of those mechanisms is difficult to establish using climate model simulations where Atlantic sea surface temperatures (SSTs) are prescribed and Pacific feedbacks cannot be realistically included. As an alternative approach, here we use a Linear Inverse Model (LIM) trained on observations and capable of correctly reproducing the observed statistics, to assess the relative role of the Atlantic-to-Pacific and Pacific-to-Atlantic influences on tropical Pacific variability. Our results indicate that Atlantic internal variability can enhance interannual SST anomalies in the eastern equatorial Pacific, and decadal SST anomalies in the central equatorial Pacific, while Pacific influences on the Atlantic significantly damp tropical Pacific decadal variability. This methodological framework could also be used to assess climate model fidelity in representing tropical basin interactions, helping to reconcile existing differences among models’ results.

A 21st century shift in the mechanisms of the early-winter United States snowfall variability

Snowfall is a critical element of natural disasters to the United States (US) with strong climatic and socioeconomic influences. Meanwhile, snowfall acts as a driving force to the US water supplies for agriculture, drinking water and hydropower. However, so far, what factors influence the US snowfall variations and how these factors change under global warming remain unclear. Here, we found that large-scale influences of the early-winter US snowfall experienced a shift from the Pacific to the Atlantic side around 2000, through observational analysis and climate model simulations. The Pacific/North American pattern was identified as a dominant driver of the early-winter US snowfall before 2000, but its impact became much weaker in the 21st century as its associated western North American cell shifted northward away from the US. Instead, the tropical and subpolar North Atlantic surface temperature has been influencing the early-winter US snowfall variations via teleconnections after 2000. This changed influence of US snowfall around 2000 is demonstrated to be related to the observed global warming pattern since the 1950s. Our study provides new perspectives in understanding large-scale snowfall pattern and variability and its connection to the global warming pattern.

Anthropogenic Intensification of Cool‐Season Precipitation Is Not Yet Detectable Across the Western United States

AbstractThe cool season (November–March) of 2022–2023 was exceptional in the western United States (US), with the highest precipitation totals in ≥128 years in some areas. Recent precipitation extremes and expectations based on thermodynamics motivate us to evaluate the evidence for an anthropogenic intensification of western US cool‐season precipitation to date. Over cool seasons 1951–2023, trends in precipitation totals on the wettest cool‐season days were neutral or negative across the western US, and significantly negative in northern California and parts of the Pacific Northwest, counter to the expected net intensification effect from anthropogenic forcing. Multiple reanalysis data sets indicate a corresponding lack of increase in moisture transports into the western US, suggesting that atmospheric circulation trends over the North Pacific have counteracted the increases in atmospheric moisture expected from warming alone. The lack of precipitation intensification to date is generally consistent with climate model simulations. A large ensemble of 648 simulations from 35 climate models suggests it is too soon to detect anthropogenic intensification of precipitation across much of the western US. In California, the 35‐model median time of emergence for intensification of the wettest days is 2080 under a mid‐level emissions scenario. On the other hand, observed reductions of precipitation extremes in California and the Pacific Northwest are near the lower edge of the large ensemble of simulated trends, calling into question model representation of western US precipitation variability.

Australian Summer Monsoon: Reanalyses Versus Climate Models in Moist Static Energy Budget Evolution

AbstractThe Australian summer monsoon (ASM) influences the tropical hydro‐climate of Northern Australia during the extended summer months (October–April). Despite advances in understanding the ASM, climate models vary widely in their depiction and projections of its future behavior remain uncertain. This study investigates the moist static energy (MSE) budget and examines the gross moist stability (GMS) evolution throughout the monsoon cycle using two reanalysis data sets. We then assess the ability of Atmospheric Modeling Intercomparison Project (AMIP) simulations of climate models to reproduce not only the monsoon seasonal cycle of rainfall but the associated mechanisms revealed by the budget analysis. The budget analysis shows a strong influence of the regions to the north and west of our study area for the import of moisture and export of energy into and away from the ASM. We find that models reproduce this influence qualitatively, but not quantitatively. As in previous studies, we identify two major regimes of the GMS associated with the absence (higher GMS) or presence (lower GMS) of convection. Whilst climate models are able to distinguish the two regimes, they significantly overestimate the GMS in convectively active periods, owing largely to profile of ascent that is too top heavy. Models with more realistic precipitation do not consistently offer more accurate representations of dynamic processes, as evaluated by the MSE budget and GMS. This highlights limitations in assessing models based solely on single variables. To enhance the generalizability of these findings, future studies should employ models without prescribed sea surface temperatures.