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Causal inference methods are important to complement predictive machine learning models to improve theoretical understanding of the underlying system in climate science. Granger causality (GC) can determine whether time series value of one variable (e.g., X) can predict the future values of another variable (e.g., Y), but not the other way around.
Here, this study evaluates high latitude stratiform mixed-phase clouds (SMPC) in the atmosphere model of the Energy Exascale Earth System Model version 2 (EAMv2) by utilizing one-year-long ground-based remote sensing measurements from the Atmospheric Radiation and Measurement (ARM) program. A nudging approach is applied to model simulations for a constrained comparison with the ARM observations. Observed and modeled SMPCs are sampled and collocated to address the difference in data resolution, so that we can consistently evaluate their macrophysical properties at the North Slope of Alaska (NSA) site in the Arctic and the McMurdo (AWR) site in the Antarctic. We found that EAMv2 overestimates SMPC frequency of occurrence at both sites. However, the model captures the observed larger cloud frequency of occurrence at the NSA site. For collocated SMPCs, the annual statistics of observed cloud macrophysics are generally reproduced at the NSA site, while at the AWR site, there are larger biases. Compared to the AWR site, the lower cloud top and cloud base and the warmer cloud top temperature observed at NSA are well simulated. On the other hand, simulated cloud phase is substantially biased. The model largely overestimates liquid water path, and the ice water path is underestimated at NSA, but at AWR, the liquid water path is frequently underestimated due to the dominance of snow in Antarctic SMPCs. As a result, the observed hemispheric difference in cloud phase partitioning is misrepresented in EAMv2. This study implies that additional model development is needed for high latitude mixed-phase clouds.
An overview of the March 2003 coordinated sea ice field campaign in the Alaskan Arctic is presented with reference to the papers in this special section. This campaign is part of the program to validate the Aqua Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice products. Standard AMSR-E sea ice products include sea ice concentration, sea ice temperature, and snow depth on sea ice. The validation program consists of three elements, namely: 1) satellite data comparisons; 2) coordinated satellite/aircraft surface measurements; and 3) modeling and sensitivity analyses. Landsat-7 and RADARSAT observations were used in comparative studies with the retrieved AMSR-E sea ice concentrations. The aircraft sensors provided high-resolution microwave imagery of the surface, atmospheric profiles of temperature and humidity, and digital records of sea ice conditions. When combined with in situ measurements, aircraft data were used to validate the AMSR-E sea ice temperature and snow-depth products. The modeling studies helped interpret the field-data comparisons, provided insight on the limitations of the AMSR-E sea ice algorithms, and suggested potential improvements to the AMSR-E retrieval algorithms.
The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.
A melt pond (MP) distribution equation has been developed and incorporated into the Marginal Ice-Zone Modeling and Assimilation System to simulate Arctic MPs and sea ice over 1979-2016. The equation differs from previous MP models and yet benefits from previous studies for MP parameterizations as well as a range of observations for model calibration. Model results show higher magnitude of MP volume per unit ice area and area fraction in most of the Canada Basin and the East Siberian Sea and lower magnitude in the central Arctic. This is consistent with Moderate Resolution Imaging Spectroradiometer observations, evaluated with Measurements of Earth Data for Environmental Analysis (MEDEA) data, and closely related to top ice melt per unit ice area. The model simulates a decrease in the total Arctic sea ice volume and area, owing to a strong increase in bottom and lateral ice melt. The sea ice decline leads to a strong decrease in the total MP volume and area. However, the Arctic-averaged MP volume per unit ice area and area fraction show weak, statistically insignificant downward trends, which is linked to the fact that MP water drainage per unit ice area is increasing. It is also linked to the fact that MP volume and area decrease relatively faster than ice area. This suggests that overall the actual MP conditions on ice have changed little in the past decades as the ice cover is retreating in response to Arctic warming, thus consistent with the Moderate Resolution Imaging Spectroradiometer observations that show no clear trend in MP area fraction over 2000-2011.
Reanalyses are regularly gridded, retrospective depictions of the physical earth system, which are produced through the correction of a short-term forecast to available observations. In the Arctic, reanalyses are particularly well suited to marshal the sparse observing network to provide a plausible, multivariate representation of conditions. Atmospheric reanalyses such as MERRA-2 (NASA Modern-Era Retrospective analysis for Research and Applications, version 2) and ocean reanalyses such as SODA3 (Univ. Maryland Simple Ocean Data Assimilation version 3) are widely used in Arctic research for diagnostic studies of circulation, model evaluation, and as boundary conditions for a variety of process models. Here, we provide examples that illustrate the utility of reanalyses for providing information on the spatial and temporal scales of recent, rapid changes in the Arctic. Recent trends in Arctic surface temperatures, surface melt over Greenland and Arctic glaciers, and evolving freshwater conditions in the Arctic Ocean are examples where reanalyses can provide information that cannot easily be obtained via other means. These examples provide information on the scale, magnitude, and the uncertainty of recent Arctic change and provide a context for future scenarios. We further quantify uncertainties in key reanalyses variables and approaches for addressing these issues.
Aerosols emitted from wildfires could significantly affect global climate through perturbing global radiation balance. In this study, the Community Earth System Model with prescribed daily fire aerosol emissions is used to investigate fire aerosols’ impacts on global climate with emphasis on the role of climate feedbacks. The total global fire aerosol radiative effect (RE) is estimated to be -0.78 ± 0.29 W m -2 , which is mostly from shortwave RE due to aerosol–cloud interactions (REaci; -0.70 ± 0.20 W m -2 ). The associated global annual-mean surface air temperature (SAT) change ΔT is -0.64 ± 0.16 K with the largest reduction in the Arctic regions where the shortwave REaci is strong. Associated with the cooling, the Arctic sea ice is increased, which acts to reamplify the Arctic cooling through a positive ice-albedo feedback. The fast response (irrelevant to ΔT) tends to decrease surface latent heat flux into atmosphere in the tropics to balance strong atmospheric fire black carbon absorption, which reduces the precipitation in the tropical land regions (southern Africa and South America). The climate feedback processes (associated with ΔT) lead to a significant surface latent heat flux reduction over global ocean areas, which could explain most (~80%) of the global precipitation reduction. The precipitation significantly decreases in deep tropical regions (5°N) but increases in the Southern Hemisphere tropical ocean, which is associated with the southward shift of the intertropical convergence zone and the weakening of Southern Hemisphere Hadley cell. Such changes could partly compensate the interhemispheric temperature asymmetry induced by boreal forest fire aerosol indirect effects, through intensifying the cross-equator atmospheric heat transport.
Abstract The subpolar North Atlantic (SPNA) shows contrasting responses in two sensitivity experiments with increased stratospheric aerosols, offering insight into the physical processes that may impact the Atlantic meridional overturning circulation (AMOC) in a warmer climate. In one, the upper ocean becomes warm and salty, but in the other it becomes cold and fresh. The changes are accompanied by diverging AMOC responses. The first experiment strengthens the AMOC, opposing the weakening trend in the reference simulation. The second experiment shows a much smaller impact. Both simulations use the Community Earth System Model with the Whole Atmosphere Community Climate Model component (CESM-WACCM) but differ in model versions and stratospheric aerosol specifications. Despite both experiments using similar approaches to increase stratospheric aerosols to counteract the rising global temperature, the contrasting SPNA and AMOC responses indicate a considerable dependency on model physics, climate states, and model responses to forcings. This study focuses on examining the physical processes involved with the impact of stratospheric aerosols on the SPNA salinity changes and their potential connections with the AMOC and the Arctic. We find that in both cases, increased stratospheric aerosols act to enhance the SPNA upper-ocean salinity by reducing freshwater export from the Arctic, which is closely tied to the Arctic sea ice changes. The impact on AMOC is primarily through the thermal component of the surface buoyancy fluxes, with negligible contributions from the freshwater component. These experiments shed light on the physical processes that dictate the important connections between the SPNA, the Arctic, the AMOC, and their subsequent feedbacks on the climate system.
Abstract The Beaufort Gyre (BG), the largest Arctic Ocean freshwater reservoir, has drastically increased its liquid freshwater content by 40% in the past two decades. If released within a short period, the excess freshwater could potentially impact the large-scale ocean circulation by freshening the upper subpolar North Atlantic. Here, we track BG-sourced freshwater using passive tracers in a global ocean sea-ice model and show that this freshwater exited the Arctic mostly through the Canadian Arctic Archipelago, rather than Fram Strait, during an historical release event in 1983–1995. The Labrador Sea is the most affected region in the subpolar North Atlantic, with a freshening of 0.2 psu on the western shelves and 0.4 psu in the Labrador Current. Given that the present BG freshwater content anomaly is twice the historical analog studied here, the impact of a future rapid release on Labrador Sea salinity could be significant, easily exceeding similar fluxes from Greenland meltwater.
The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow, and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth System Models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems).The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (LMIP, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (LFMIP, building upon the GLACE-CMIP blueprint).
This study performs a comprehensive evaluation of the simulated cloud phase in the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) atmosphere model version 2 (EAMv2) and version 1 (EAMv1). Enabled by the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) simulator, EAMv2 and EAMv1 predicted cloud phase is compared against the GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP) at high latitudes where mixed-phase clouds are prevalent. Our results indicate that the underestimation of cloud ice in simulated high-latitude mixed-phase clouds in EAMv1 has been significantly reduced in EAMv2. The increased ice clouds in the Arctic mainly result from the modification on the WBF (Wegner-Bergeron-Findeisen) process in EAMv2. The impact of the modified WBF process is moderately compensated by the low limit of cloud droplet number concentration (CDNC) in cloud microphysics and the new dCAPE_ULL trigger used in deep convection in EAMv2. Moreover, it is found that the new trigger largely contributes to the better cloud phase simulation over the Norwegian Sea and Barents Sea in the Arctic and the Southern Ocean where large errors are found in EAMv1. However, errors in simulated cloud phase in EAMv1, such as the overestimation of supercooled liquid clouds near the surface in both hemispheres and the underestimation of ice clouds over Antarctica, persist in EAMv2. In conclusion, this study highlights the impact of deep convection parameterizations, which has received little attention, on high-latitude mixed-phase clouds, and the importance of continuous improvement of cloud microphysics in climate models for accurately representing mixed-phase clouds.
This grant provided partial support for participation in the SAGE III Ozone Loss and Validation Experiment. The NASA-sponsored SOLVE mission was conducted Jointly with the European Commission-sponsored Third European Stratospheric Experiment on Ozone (THESEO 2000). Researchers examined processes that control ozone amounts at mid to high latitudes during the arctic winter and acquired correlative data needed to validate the Stratospheric Aerosol and Gas Experiment (SAGE) III satellite measurements that are used to quantitatively assess high-latitude ozone loss. The campaign began in September 1999 with intercomparison flights out of NASA Dryden Flight Research Center in Edwards. CA. and continued through March 2000. with midwinter deployments out of Kiruna. Sweden. SOLVE was co-sponsored by the Upper Atmosphere Research Program (UARP). Atmospheric Effects of Aviation Project (AEAP). Atmospheric Chemistry Modeling and Analysis Program (ACMAP). and Earth Observing System (EOS) of NASA's Earth Science Enterprise (ESE) as part of the validation program for the SAGE III instrument.
Surface heterogeneity in the marginal ice zone (MIZ) causes multiscale secondary atmospheric circulations that are challenging to model or observe. The absence or inadequate representation of these circulations in ocean‐atmosphere exchange schemes in climate models is partially responsible for the underestimation of Arctic sea ice loss. Observationally, such circulations obfuscate the interpretation of polar atmospheric chemistry measurements, among others. To address this open challenge, large‐eddy simulations are conducted over real‐world satellite‐sensed sea ice maps with an ice‐sea temperature contrast, as well as over idealizations of these maps that alter the ice pattern but conserve its fraction, showing that the ice fraction of a surface is not sufficient to predict the surface heat flux. In a second suite of simulations, three other heterogeneity metrics (representing the surface fragmentation, patch edge tortuosity, and patch size variability) are introduced to complement the ice fraction in describing the surface. Simulations varying these parameters suggest that they also significantly impact surface‐air interactions. A roughness contrast is then added to the surface temperature contrast, indicating that the contribution of roughness changes to the resulting atmospheric circulations is less pronounced than that of thermal heterogeneity. Based on these results, we illustrate, using a multi‐linear regression on these map features, that MIZ surface parameterizations in Earth Systems Models can be improved if they account for these various characteristics of the sea‐ice patterns.
Abstract Rapid warming is likely increasing primary production and wildfire occurrence in the Arctic. Projected changes in carbonaceous aerosols during the summer will impact atmospheric chemistry and climate, but our understanding of these processes is limited by sparse observations. Here, we characterize carbonaceous aerosol in Alaska, USA: Toolik Field Station in the Interior and the Atmospheric Radiation Measurement facility at Utqiaġvik on the Arctic coast, during the summers of 2022 and 2023. We estimated PM 2.5 and PM 10 concentrations using laser light scattering (PurpleAir sensors) and examined total carbon (TC) and its organic carbon (OC) and elemental carbon (EC) fractions in total suspended particles (TSP). We investigated the dominant sources of carbonaceous aerosol using air mass backward‐trajectories from the NOAA HYSPLIT model and radiocarbon source apportionment of TC. TC concentrations were about twice as high in the Interior compared to the coast, with contemporary sources dominating at both Toolik (95%–99%) and Utqiaġvik (86%–89%) over minor contributions from fossil sources. Elevated PM, TC, OC, and EC concentrations coincided with major boreal forest fire activity in North America that brought smoke to the region. The radiocarbon signature of EC measured at Toolik during these wildfire events indicated that over 90% of the EC came from contemporary sources. Our measurements demonstrate the potential for Arctic aerosol concentrations to respond significantly to climate warming‐induced changes to the landscape and emphasize the need for continuous atmospheric monitoring to advance our understanding of this rapidly changing environment.
Throughout the Proterozoic Era, sedimentary organic carbon burial helped set the pace of global oxygenation and acted as a major modulator of atmospheric CO 2 and climate. Although Proterozoic rocks generally contain low concentrations of organic matter (OM), there are key exceptions to this rule, including the relatively OM-rich Arctic Bay shales from Baffin Island, Canada (Bylot Supergroup, Borden Basin, ~1.05 Ga). The mechanisms driving elevated OM concentrations in these and other Proterozoic shales remain poorly understood. In the Mesozoic and Cenozoic, organic matter sulfurization can be a major driver of enhanced OM burial across a range of redox conditions comparable to those inferred for many Proterozoic environments. Therefore, in this study, we evaluate the role of sulfurization in driving OM preservation in the Mesoproterozoic Borden Basin and discuss its relevance to Proterozoic systems in general. We present the first evidence for syngenetic-to-early-diagenetic OM sulfurization in a Proterozoic basin, which begins to fill a several-billion-year gap in our record of organic S across Earth history. We find that OM sulfurization was particularly extensive in shales from a relatively shallow-water section (Alpha River) but less extensive in shales deposited in deeper water (Shale Valley), which is consistent with models that infer sulfidic ‘wedges’ or O 2 -minimum-zone-type structures on shelf margins at least intermittently at this time. At the shallower site, organic S and pyrite are similarly 34 S-depleted and thus likely formed at roughly the same time near the sediment–water interface under conditions previously interpreted to have been ferruginous to intermittently sulfidic. In contrast, at the deeper-water site, large S-isotope differences between pyrite and organic S along with low apparent OM sulfurization intensities indicate that pyrite formation was favored over OM sulfurization during early sedimentation under variable but primarily ferruginous conditions. Although Mesoproterozoic biomass can be substantially sulfurized, indicators of sulfurization intensity are not correlated with OM concentrations, and therefore sulfurization does not appear to have been the primary driver of enhanced OM concentrations in Arctic Bay Formation shales. The link between sulfurization and total OM preservation may have been modulated during the deposition of Arctic Bay Formation shales by differences in iron availability, nutrient cycling, and particle dynamics in the Mesoproterozoic.
Ensembles of numerical forecasts based on perturbed initial conditions have long been used to improve estimates of both weather and climate forecasts. The Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model, Version 5 (GEOS-5 AOGCM) Seasonal-to-Interannual Forecast System has been used routinely by the GMAO since 2008, the current version since 2012. A coupled reanalysis starting in 1980 provides the initial conditions for the 9 month experimental forecasts. Once a month, sea surface temperature from a suite of 11 ensemble forecasts is contributed to the North American Multi-Model Ensemble (NMME) consensus project, which compares and distributes seasonal forecasts of ENSO events. Since June 2013, GEOS-5 forecasts of the Arctic sea-ice distribution were provided to the Sea-Ice Outlook project. The seasonal forecast output data includes surface fields, atmospheric and ocean fields, as well as sea ice thickness and area, and soil moisture variables. The current paper aims to document the characteristics of the GEOS-5 seasonal forecast system and to highlight forecast biases and skills of selected variables (sea surface temperature, air temperature at 2 m, precipitation and sea ice extent) to be used as a benchmark for the future GMAO seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems.
Data was derived using daily runoff from models participating in the Coupled Model Intercomparison Project, version 6 (CMIP6) that have been routed through the Model for Scale Adaptive River Transport (MOSART) routing model to represent streamflow at individual streamflow gage locations. Data from 750 streamflow gage locations are represented, which are indexed by gage id according to the metadata.csv file. Streamflow gage location sites were based on gage records downloaded from the United States Geological Survey (USGS), National Water Data (HYDAT) Archive from Canada, and the State Hydrological Institute of Russia. Data are available from 11 Earth System Models (ESMs) in total at a daily timestep with a duration of 1920-2099 (1850-2014 for E3SM only). The spatial domain of the modeled data is 0.5 degree resolution. Earth System Models and Units of mean daily streamflow are presented in cubic meters per second (cms). Data files consist of 11 Earth System Model mean daily streamflow output per gage location in .csv format, 1 metadata file in .csv format and 1 Readme file in .docx format (13 files in total). These data were used to benchmark CMIP6 modeled representations of streamflow against gage records.
As atmospheric temperatures rise due to increased anthropogenic forcing, the effect is expected to be larger over the arctic than midlatitude and tropics, known as polar amplification. A multi-national program, the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) was ran between Sept 2019 and Oct 2020. The project deployed scientific instruments on board the German research vessel Polarstern with the ship remaining across a year to observe all aspects of polar climate. The US DOE deployed the ARM AMF2 aboard the ship along with several off-ship sites for extended spatial observations. Of particular importance was the measurement of the energetics of the ice/ocean/atmosphere interactions through observations of surface irradiance and surface temperatures. NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project produces the SYN1deg Edition 4 data product. This product provides high quality, hourly, globally gridded and temporally complete maps of top of atmosphere (TOA), in atmosphere, and surface irradiances. TOA fluxes are derived from CERES instruments and geostationary satellites. In addition, TOA, in atmosphere, and surface irradiances are computed using the Langley Fu and Liou radiative transfer model. The radiative transfer model is run hourly at 1 degree ~equal area spatial resolution. Meteorological profiles are provided by Global Modeling and Assimilation Office’s GEOS-541 reanalysis product. Cloud properties are derived solely from Terra and Aqua MODIS imagers northward of 60°. Here we compare SYN1deg hourly calculations of surface irradiance to observations from the AMF2 to validate the products estimates of surface irradiance in this challenging area. Along with the irradiance comparisons we take a close look at the surface temperature record in the GEOS-541 product and compare it over both time and space to several surface observations provided by MOSAiC. Along with the comparison to the re-analysis record we will compare these observed surface temperatures to those derived from the AIRS product, which is known to have some difficulty in extreme high latitude areas. The goals of this study are 1) understand computed surface downward irradiance and temperature error separated by surface type (e.g. sea ice or open water) and by season, and 2) error covariance in spatial and temporal space. We seek to use this information to extrapolate the error from the MOSAiC domain to a larger arctic region.