A document by Nathan Lenssen. Click on the document to view its contents.

Increasing snow accumulation over the Antarctic Ice Sheet may mitigate future sea level rise. However, current estimates of mitigation potential are poorly constrained due to limited records of past variability. We present an annually resolved reconstruction of Antarctic snow accumulation from 1801 to 2000 CE, employing a paleoclimate data assimilation methodology to integrate ice core records with a multi-model ensemble of climate simulations. Our reconstruction correlates well with instrumental reanalysis, and we find that Antarctic accumulation rates increased over the 20th-century, resulting in a modest amount (~1 mm) of sea level mitigation. Mitigation is primarily driven by an accelerating trend since around 1970. Our results contrast with a previous mitigation estimate of ~10 mm; this discrepancy is due to unconstrained baseline estimates of 19th-century accumulation in East Antarctica. Our reconstruction suggests that the uncertainty of future sea level mitigation from increasing Antarctic accumulation has been underestimated.

Initialized climate model simulations have proven skillful for near-term predictability of the key physical climate variables. By comparison, predictions of biogeochemical fields like ocean carbon flux, are still emerging. Initial studies indicate skillful predictions are possible for lead-times up to six years at global scale for some CMIP6 models. However, unlike core physical variables, biogeochemical variables are not directly initialized in existing decadal preciction systems, and extensive empirical parametrization of ocean-biogeochemistry in Earth System Models introduces a significant source of uncertainty. Here, we propose a new approach for improving the skill of decadal ocean carbon flux predictions using observationally-constrained statistical models, as alternatives to the ocean-biogeochemistry models. We use observations to train multi-linear and neural-network models to predict the ocean carbon flux. To account for observational uncertainties, we train using six different observational estimates of the flux. We then apply these trained statistical models using input predictors from the Canadian Earth System Model (CanESM5) decadal prediction system to produce new decadal predictions. Our hybrid GCM-statistical approach significantly improves prediction skill, relative to the raw CanESM5 hindcast predictions over 1990-2019. Our hybrid-model skill is also larger than that obtained by any available CMIP6 model. Using bias-corrected CanESM5 predictors, we make forecasts for ocean carbon flux over 2020-2029. Both statistical models predict increases in the ocean carbon flux larger than the changes predicted from CanESM5 forecasts. Our work highlights the ability to improve decadal ocean carbon flux predictions by using observationally-trained statistical models together with robust input predictors from GCM-based decadal predictions.

The third phase of the Cloud Feedback Model Intercomparison Project requested that modeling centers perform a pair of simulations where the climate system is subjected to an abrupt change of the solar constant by +/- 4%. The forcing is designed to loosely match the amount of radiative forcing incurred by quadrupling atmospheric CO2 concentrations. Using these simulations, we examine how clouds respond to changes in solar forcing and act as a feedback on global surface temperature. Specifically, in this paper, we study the temperature mediated cloud changes that occur following an abrupt increase and decrease of the solar constant and compare with temperature mediated cloud changes that occur following quadrupling and halving of CO2. We seek to answer two primary questions: 1) How do cloud feedbacks differ in response to abrupt changes in CO2 and solar forcing? And 2) Are there symmetrical (equal and opposite) cloud feedbacks to an increase and a decrease in solar forcing? We find that temperature mediated cloud changes are similar from increasing solar and CO2 forcing, with the only robust difference being that there is a larger reduction of low cloud amount following solar forcing; and we find that cloud responses to warming and cooling are not symmetric, due primarily to non-linearity introduced by phase changes in mid-to-high latitude low clouds, and sea ice loss/formation.

In this paper we examine differences in cloud adjustments (often called rapid adjustments) that occur as a direct result of abruptly increasing the solar constant by 4% or abruptly quadrupling of atmospheric CO2. In doing so, we devised a novel method for calculating the cloud adjustments for the abrupt solar forcing experiment that uses differences between coupled model simulations with abrupt solar and CO2 forcing, in combination with uncoupled, atmosphere-only, abrupt CO2 forced experiments that have prescribed sea-surface temperature. Our main findings are that 1) there are substantial differences in the response of stratocumulus and cumulus clouds to solar and CO2 forcing, which follow the differences in the direct radiative effect that solar and CO2 forcing have at cloud top, and 2) there are differences in the adjustment of the average optical depth of high clouds to solar and CO2 forcing that we speculate are driven by the differences in the vertical profile of radiative heating, and differences in the pattern of sea-surface temperature change (for a fixed global mean temperature). Such adjustments do contribute significantly to the total net cloud radiative effect, even after 150 years of simulation.

Reconstructing past changes in global mean surface temperature (GMST) is one of the key contributions that palaeoclimate science can make in addressing societally relevant questions and is required to determine equilibrium climate sensitivity (ECS). Previous work has suggested that the temperature of the deep ocean (Td) can be used to determine GMST with a simple Td-GMST scaling factor of 1 prior to the Pliocene. However, this metric lacks a robust mechanistic basis, and indeed, such a relationship is intuitively difficult to envisage given that polar amplification is a ubiquitous feature of past warm climate states and deep water overwhelmingly forms at high latitudes. Here, we interrogate whether and crucially, why, this relationship exists using a suite of curated data compilations generated for key deep-time climate intervals as well as two independent sets of palaeoclimate model simulations. We show that models and data are in full agreement that a 1:1 relationship is a good approximation. Mechanistically, both sets of climate models suggest that i) increasingly seasonally biased deep water formation, and ii) a faster rate of land versus ocean surface warming are the two processes that act to counterbalance a possible polar amplification-derived bias on Td-derived GMST. Using this knowledge, we interrogate the quality of the existing deep ocean temperature datasets and provide a new Cenozoic record of GMST. Our estimates are substantially warmer than similar previous efforts for much of the Paleogene and are thus consistent with a substantially higher-than-modern ECS during deep-time high CO2 climate states.

Low-level jets (LLJs), wind speed maxima in the lower troposphere, impact several environmental and societal phenomena. In this study we take advantage of the spatially and temporally complete meteorological dataset from ERA5 to present a global climatology of LLJs taking into consideration their formation mechanisms, characteristics and trends during the period of 1992-2021. The global mean frequency of occurrence was of 21% with values of 32% and 15% for land and ocean. We classified the LLJs into three regions: non-polar land (LLLJ), polar land (PLLJ) and coastal (CLLJ). Over LLLJ regions, the average frequency of occurrence was of 20%, with 75% of them associated with a near-surface temperature inversion i.e. associated with inertial oscillation at night. Over PLLJ regions the LLJs were also associated with a temperature inversion, but were much more frequent (59%), suggesting other driving mechanisms than the nocturnal inversion. They were also the lowest and the strongest LLJs. CLLJs were very frequent in some hotspots, specially on the west coast of the continents, with neutral to unstable stratification close to the surfaces, that became more stably stratified with increasing height. We found distinct regional trends in both the frequency and intensity of LLJs, potentially leading to changes in the emission and transport of dust aerosols, polar ice and moisture over the world. However, it is currently unclear the evolution of the trends with global warming and what the implications are for climate and weather extremes. Future studies will investigate long-term trends for LLJs and the associated implications.

As part of the REgional Carbon Cycle Assessment and Processes Phase 2 (RECCAP2) project, we developed a comprehensive African Greenhouse gases (GHG) budget for the period 2010-2019 and compared it to the budget over the 1985-2009 (RECCAP1) period. We considered bottom-up process-based models, data-driven remotely sensed products, and national GHG inventories in comparison with top-down atmospheric inversions, accounting also for lateral fluxes. We incorporated emission estimates derived from novel methodologies for termites, herbivores, and fire, which are particularly important in Africa. We further constrained global woody biomass change products with high-quality regional observations. During the RECCAP2 period, Africa’s carbon sink capacity is decreasing, with net ecosystem exchange switching from a small sink of −0.61 ± 0.58 PgCyr−1 in RECCAP1 to a small source in RECCAP2 at 0.162 (-1.793/2.633) PgCyr-1. Net CO2 emissions estimated from bottom-up approaches were 1.588 (-6.461/11.439) PgCO2yr-1, net CH4 were 78.453 (36.665/59.677) TgCH4yr-1) and net N2O were 1.81 (1.716/2.239) TgN2Oyr-1. Top-down atmospheric inversions showed similar trends. LUC emissions increased, representing one of the largest contributions at 1.746 (0.841/2.651) PgCO2eq yr-1 to the African GHG budget and almost similar to emissions from fossil fuels at 1.743 (1.531/1.956) PgCO2eq yr-1, which also increased from RECCAP1. Additionally, wildfire emissions decreased, while fuelwood burning increased. For most component fluxes, uncertainty is large, highlighting the need for increased efforts to address Africa-specific data gaps. However, for RECCAP2, we improved our overall understanding of many of the important components of the African GHG budget that will assist to inform climate policy and action.

Clear-Air Turbulence (CAT) is associated with wind shear in the vicinity of jet streams in upper atmospheric levels. This turbulence occurs in cloudless regions and causes most weather-related aircraft accidents. Recent studies have shown that in response to climate change, CAT could significantly increase over certain regions as a consequence of strengthening of jet streams. In this study we use several atmospheric reanalyses and coupled model experiments database to evaluate CAT recent and future changes in the Northern Hemisphere. Several CAT diagnostics are computed to assess the sensitivity of results to different turbulence representations. A significant positive trend in CAT frequency is found in the reanalyses in different regions in the Northern Hemisphere over the period 1980-2021. The signal-to-noise analysis shows that over North Africa, East Asia and Middle East the increase of CAT occurrence in the last decades is likely attributed to global warming. In contrast, over the North Atlantic and North Pacific the internal climate variability is too strong to detect a response to anthropogenic forcing in the observed trends. Future climate projections show that over several regions in the Northern Hemisphere, CAT is projected to increase with a high model agreement and independently of the CAT diagnostic used. The largest increase in CAT is projected to occur over East Asia. In the North Atlantic, large uncertainty remains due to lack of model agreement and differences among the various CAT diagnostics.

A document by Ben Cala. Click on the document to view its contents.

A document by Alexa Terrazas. Click on the document to view its contents.

Nitrous oxide (N2O) is a greenhouse gas and an ozone-depleting agent with large and growing anthropogenic emissions. Previous studies identified the influx of N2O-depleted air from the stratosphere to partly cause the seasonality in tropospheric N2O (aN2O), but other contributions remain unclear. Here we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N2O Model Intercomparison Project with tropospheric transport modeling to simulate aN2O at the air sampling sites: Alert, Barrow, Ragged Point, Samoa, Ascension Island, and Cape Grim for the modern and preindustrial periods. Models show general agreement on the seasonal phasing of zonal-average N2O fluxes for most sites, but, seasonal peak-to-peak amplitudes differ severalfold across models. After transport, the seasonal amplitude of surface aN2O ranges from 0.25 to 0.80 ppb (interquartile ranges 21-52% of median) for land, 0.14 to 0.25 ppb (19-42%) for ocean, and 0.13 to 0.76 ppb (26-52%) for combined flux contributions. The observed range is 0.53 to 1.08 ppb. The stratospheric contributions to aN2O, inferred by the difference between surface-troposphere model and observations, show 36-126% larger amplitudes and minima delayed by ~1 month compared to Northern Hemisphere site observations. Our results demonstrate an increasing importance of land fluxes for aN2O seasonality, with land fluxes and their seasonal amplitude increasing since the preindustrial era and are projected to grow under anthropogenic activities. In situ aN2O observations and atmospheric transport-chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting surface N2O sources under ongoing global warming.

When different flooding drivers co-occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data-proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36% to 49% by 2080, depending on the scenario. The large-scale changes in the joint probability of extreme wind speed and precipitation are similar, but locally, differences between the changes in the two types of compound extremes can exceed the changes themselves. Due to internal climate variability and inter-model differences, projections based on only 5 to 6 random climate model simulations have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the 21st century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections.

Stratospheric aerosol injection (SAI has been proposed as a potential method for mitigating risks and impacts associated with anthropogenic climate change. One such risk is widespread permafrost thaw and associated carbon release. While permafrost has been shown to stabilize under different SAI scenarios, natural variability may lead to a wide range of projected climate futures under SAI. Here we use the 10-member ensemble from the ARISE-SAI-1.5 simulations to assess the spread in projected active layer depth and permafrost temperature across boreal permafrost soils and specifically in four peatland and Yedoma regions. The forced response in active layer depth and permafrost temperature quickly diverge between an SAI and non-SAI world, but individual ensemble members overlap for several years following SAI deployment. Projected permafrost variability may mask the forced response to SAI and make it difficult to detect if and when SAI is stabilizing permafrost in any single realization. We find that it may take more than a decade of SAI deployment to detect the effects of SAI on permafrost temperature and almost 30 years to detect its effects on active layer depth. Not only does natural variability make it more difficult to detect SAI’s influence, it could also affect the likelihood of reaching a permafrost tipping point. In some realizations, SAI fails to prevent a tipping point that is also reached in a non-SAI world. Our results underscore the importance of accounting for natural variability in assessments of SAI’s potential influence on the climate system.

Relic coastal landforms (fossil corals, cemented intertidal deposits, or erosive features carved onto rock coasts) serve as sea-level index points (SLIPs) widely used to reconstruct past sea-level changes. Traditional SLIP-based sea-level reconstructions face challenges in capturing continuous sea-level variability and dating erosional outcrops, such as ubiquitous tidal notches, carved around tidal level on carbonate cliffs. We propose a novel approach to such challenges by using a numerical cliff erosion model embedded within a Monte-Carlo simulation to investigate the most likely sea-level scenarios responsible for shaping one of the best-preserved tidal notches of the Last Interglacial age in Sardinia, Italy. Results align with Glacial Isostatic Adjustment model predictions, indicating that synchronized or out-of-sync ice-volume shifts in Antarctic and Greenland ice sheets can reproduce the notch morphology, with sea level confidently peaking at 6m. This new approach yields continuous sea-level insights, bridging gaps in traditional methods and illuminating past Interglacial sea-level dynamics.

There is a need to improve the prediction of tropical cyclone (TC) rainfall as climate change has led to increased TC rainfall rates. Enhanced reliability in predicting TC tracks has paved the way for statistical methodologies to utilize them in estimating current TC rainfall, achieved by identifying similar past TC tracks and obtaining their corresponding rainfall data. The widely used Fuzzy C Means (FCM) clustering algorithm, though popular, has limitations stemming from its clustering-centric design, hindering its ability to pinpoint the most appropriate similar TCs. Our study introduces the Sinkhorn Distance as a novel measure of TC similarity in rainfall prediction. Our findings indicate that the incorporation of Sinkhorn Distance significantly enhances the accuracy of TC rainfall predictions across WNP. When compared to the conventional approach using FCM, our Sinkhorn Distance-based methodology consistently yields better results, as demonstrated by metrics like RMSE and correlation coefficients. Collectively, the inclusion of Sinkhorn Distance stands as a valuable addition to our toolkit for discerning similar TC tracks, thus elevating the precision of TC rainfall predictions. With ongoing advancements in statistical and AI techniques, we anticipate even more refined approaches to further enhance our predictive capabilities. This study represents a leap forward in meeting the critical need for more accurate TC rainfall forecasts in the WNP Region.

Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the United States. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3 million wildfires from 1992-2020 in the United States. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including development of machine learning models.

NOAA’s Daily Optimum Interpolation Sea Surface Temperature (DOISST) indicates that globally averaged sea surface temperature (SST) broke record in March 2023 and set new record highs in April, July, and August 2023. This has raised intense media interest and public concern about causes and connections to climate change. Our analysis indicates that the record high SSTs qualified as marine heatwaves (MHWs) and even super-MHWs as defined in this study, and are attributed to three factors: (i) a linear trend, (ii) a shift to the warm phase of the multi-decadal Pacific-Atlantic-Arctic Oscillation (PAO) pattern which is identified in this study, and (iii) the transition from the triple-dip succession of La Niña events to the 2023 El Niño event. One-Sentence Summary The extreme warm SSTs in 2023 resulted from linear warming trends, a pattern of low-frequency oscillation, and the El Niño event.

A document by Aodhan John Sweeney. Click on the document to view its contents.

Clouds are critical to the biodiversity and function of Tropical Montane Cloud Forests (TMCF). These ecosystems provide vital services to humanity and are considered hotspots of endemism, given that the number of species is restricted to their microclimates. Cloudiness (e.g., the fraction of low-clouds) in these ecosystems is projected to decline owing to global warming, but recent temporal trends remain unclear. Here, we evaluated trends in low-cloud fractions (CF) and other Essential Climatic Variables (ECV) (e.g., surface temperature, pressure, soil moisture, and precipitation) for 521 sites worldwide with TMFCs from 1997 to 2020. Thus, we hypothesize that recent traces of global warming over the last few decades have led to decreases in low-cloud cover on TMCFs. The previous study was also evaluated globally and among biogeographic realms to identify regional trends. We computed trends by aggregating hourly observations from ERA5 reanalysis and CHIRPS into annual averages and then used linear regressions to calculate slopes (i.e., rate of change) (Δ, year-1). Our results suggest that CF trends at the TMCFs range between -64.7 ×10-4 and 51.4 ×10-4 CF year-1, revealing that 70% of the assessed sites have experienced reductions in CF. Declines in low-clouds in these ecosystems are 253% more severe than tropical landmasses when peak values of density distribution are compared (TMCFs: -7.8 ×10-4CF year-1; tropical landmasses -2.3 ×10-4 CF year-1). Despite this, CF trends tend to differ among biogeographic realms, as those TMCFs from the Neotropics and Indomalayan realms have the most pronounced declines. Decreases in CF were also associated with increases in surface temperature and pressure and decreases in soil moisture, revealing that the TMCF’s climate is changing to warmer environments. These climatic shifts may represent a fingerprint of global change on TMCFs, highlighting a current threat to species and essential ecosystem services that these ecosystems provide.

Quantifying sector-resolved methane fluxes in complex emissions environments is challenging yet necessary to improve emissions inventories and guide policy.  Here, we separate energy and agriculture sector emissions using a dynamic linear model analysis of methane, ethane, and ammonia data measured at a Northern Colorado site from November 2021 to January 2022. By combining these sector-apportioned observations with spatially resolved inventories and Bayesian inverse methods, energy and agriculture methane fluxes are optimized across the study’s ~850 km2 sensitivity area. Energy sector fluxes are synthesized with previous literature to evaluate trends in energy-sector methane emissions. Optimized agriculture fluxes in the study area were 3× larger than inventory estimates; we demonstrate this discrepancy is consistent with differences in the modeled vs. real-world spatial distribution of agricultural sources. These results highlight how sector-apportioned methane observations can yield multi-sector inventory optimizations in complex environments.

A document by Ron baiman. Click on the document to view its contents.

In this study, we explore the relationship between anvil cloud fraction and horizontal model resolution in small domain radiative-convective equilibrium (RCE) simulations, building on the findings of \citeA{jeevanjee22}. Using the System of Atmosphere Modeling (SAM) model, we find that finer resolutions yield higher anvil cloud fractions due to larger convective updrafts mass flux and increased mass detrainment at anvil levels. Employing two different microphysics schemes, we illustrate that finer resolution can enhance mass flux through either stronger cloud evaporation or weaker upper-troposphere stability, as the consequence of enhanced horizontal mixing. Moreover, we refine an analytical zero-buoyancy plume model to investigate the effects of adjusting entrainment rate and evaporation rate on vertical atmosphere profiles in a simple theoretical framework. Our solutions of the zero-buoyancy plume model suggest that stronger horizontal mixing can lead to larger convective updraft mass flux, consistent with the analysis in numerical simulations. We also observe the likelihood of atmospheric profiles converging at a grid size of approximately 100m, potentially as a result of converging entrainment rate and mixing strength. These insights have implications for global storm-resolving simulations, implying a possible convergence of high cloud and deep convection properties as the horizontal resolution approaches around 100m.

This study assesses less-explored Southern Ocean sea-ice parameters, namely Sea-ice Thickness and Volume, through a comprehensive comparison of 26 CMIP6 models with reanalyses and satellite observations. Findings indicate that models replicate the mean seasonal cycle and spatial patterns of sea-ice thickness, particularly during its maxima in February. However, some models simulate implausible historical mean states compared to satellite observations, leading to large inter-model spread. September sea-ice thickness is consistently biased low across the models. Our results show a positive relationship between modeled mean sea-ice area and thickness in September (i.e., models with more area tend to have thicker ice); in February this relationship becomes negative. While CMIP6 models demonstrate proficiency in simulating Area, thickness accuracy remains a challenge. This study, therefore, highlights the need for improved representation of Antarctic sea-ice processes in models for accurate projections of thickness and volume changes.