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At least 379 records · Page 21

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗

Cloud Feedback Uncertainty in the Equatorial Pacific Across CMIP6 Models

Cloud feedback is the largest uncertainty in estimating Equilibrium Climate Sensitivity. In this study we focus on the equatorial Pacific, where CMIP6 model cloud feedback spread is notably large. Cloud radiative effects in this region are relevant for the global climate. Our findings show that models predict a consistent shift towards the ascent regime in response to El Nino-like sea surface warming. Models diverge in terms of the radiative impact due to differences in cloud characteristics in ascent and subsidence regimes. Using the observed relationship between circulation regime and cloud radiative effect, we find a reduction in the regional mean cloud feedback estimate from 0.77 to 0.22 W m -2 K -1 , though this does not substantially lessen the model spread in total feedback. Pathways to reduce this spread include: improving confidence in the regional ocean warming pattern, and using observations and models to understand cloud type and circulation interactions.

CMIP6↗

Sensitivity of Regional Climate to Deforestation in the Amazon Basin

The deforestation results in several adverse effect on the natural environment. The focus of this paper is on the effects of deforestation on land-surface processes and regional climate of the Amazon basin. In general, the effect of deforestation on climate are likely to depend on the scale of the defrosted area. In this study, we are interested in the effects due to deforestation of areas with a scale of about 250 km. Hence, a meso-scale climate model is used in performing numerical experiments on the sensitivity of regional climate to deforestation of areas with that size. It is found that deforestation results in less net surface radiation, less evaporation, less rainfall, and warmer surface temperature. The magnitude of the of the change in temperature is of the order 0.5 C, the magnitudes of the changes in the other variables are of the order of IO%. In order to verify some of he results of the numerical experiments, the model simulations of net surface radiation are compared to recent observations of net radiation over cleared and undisturbed forest in the Amazon. The results of the model and the observations agree in the following conclusion: the difference in net surface radiation between cleared and undisturbed forest is, almost, equally partioned between net solar radiation and net long-wave radiation. This finding contributes to our understanding of the basic physics in the deforestation problem.

Eltahir, Elfatih A. B.↗

Applying NIR and MIR spectroscopy for C and soil property prediction in northern cold-region ecosystems. Which approach works better?

Here, developing reliable predictions of soil attributes is necessary to understand northern cold-region climate-soil feedback. Calibration models using near-infrared (NIR) and mid-infrared (MIR) spectroscopy were developed to predict eight commonly measured soil properties for 119 soil samples representing a range of vegetation types, parent materials, and soil types spanning >23° of latitude from southeast Alaska to the Canadian high Arctic. In order to obtain a more accurate prediction, this study compared the performance of linear and non-linear calibration techniques, including lasso regression (Lasso), support vector machine (SVM), random forest (RF) and classic partial least squares (PLS) to predict different soil properties of these soils. Comparing the four models, we noticed that their performance was quite similar for MIR overall, while NIR achieved better results with a PLS model for our dataset. PLS coupled with MIR showed a better performance for soil parameters, such as total organic carbon (TOC), total nitrogen (TN), cation exchange capacity (CEC) and clay (R-squared of 0.9, 0.81, 0.80, and 0.84) when compared with NIR (R-squared of 0.85, 0.72, 0.81 and 0.68). However, using either MIR or NIR spectroscopy, PLS predictions for bulk density (BD) and sand content were not accurate. The variable importance analysis based on the PLS model successfully estimated the relative contribution of wavelengths influencing soil property predictions most. Overall, TOC, TN, CEC and clay mineral predictions are closely related to the occurrence of specific spectral bands in the MIR region. For example, wavelengths at 2978 and 1761 cm -1 for TOC and TN, as well as at 3064 cm -1 for CEC, were selected as the most influential predictor variables. We demonstrated that MIR spectroscopy is a powerful tool for more extensive monitoring in soils of the northern cold climate region; however, NIR could be utilized for rapid estimates when the highest accuracy is not essential.

54 ENVIRONMENTAL SCIENCES↗

E3SM‐Arctic: Regionally Refined Coupled Model for Advanced Understanding of Arctic Systems Interactions

Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1-Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1-Arctic) historical simulations (1950–2014) with the uniform low-resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large-scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high-resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales.

Energy Exascale Earth System Model (E3SM)↗

E3SM‐GCAM: A Synchronously Coupled Human Component in the E3SM Earth System Model Enables Novel Human‐Earth Feedback Research

Abstract Modeling human‐environment feedbacks is critical for assessing the effectiveness of climate change mitigation and adaptation strategies under a changing climate. The Energy Exascale Earth System Model (E3SM) now includes a human component, with the Global Change Analysis Model (GCAM) at its core, that is synchronously coupled with the land and atmosphere components through the E3SM coupling software. Terrestrial productivity is passed from E3SM to GCAM to make climate‐responsive land use and CO 2 emission projections for the next 5‐year period, which are interpolated and passed to E3SM annually. Key variables affected by the incorporation of these feedbacks include land use/cover change, crop prices, terrestrial carbon, local surface temperature, and climate extremes. Regional differences are more pronounced than global differences because the effects are driven primarily by differences in land use. This novel system enables a new type of scenario development and provides a powerful modeling framework that facilitates the addition of other feedbacks between these models. This system has the potential to explore how human responses to climate change impacts in a variety of sectors, including heating/cooling energy demand, water management, and energy production, may alter emissions trajectories and Earth system changes. Plain Language Summary Earth system models help us understand how humans are changing the climate. Currently, these models do not include human systems, so predetermined greenhouse gas, aerosol, and land use change data are input to these models. These data do not reflect human responses to changes projected by Earth system models. We have added a human component to an Earth system model to represent human responses to environmental change and calculate corresponding greenhouse gas and land use change data instead of using predetermined data. Including this human component changes projections of land use, land carbon storage, and regional climate. Key Points We have incorporated a novel, advanceable human component in an Earth system model to simulate human‐Earth feedbacks Including terrestrial productivity feedbacks from the Earth to the human systems affects land change, crop prices, carbon, and climate Regional effects of including terrestrial productivity feedbacks are greater than global effects because land change is the main driver

E3SM↗

Albedo of the South Pole on Mars Determined by Topographic Forcing of Atmosphere Dynamics

The nature of the martian south polar cap has remained enigmatic since the first spacecraft observations. In particular, the presence of a perennial carbon dioxide ice cap, the formation of a vast area of black slab ice known as the Cryptic region and the asymmetric springtime retreat of the cap have eluded explanation. Here we present observations and climate modelling that indicate the south pole of Mars is characterized by two distinct regional climates that are the result of dynamical forcing by the largest southern impact basins, Argyre and Hellas. The style of surface frost deposition is controlled by these regional climates. In the cold and stormy conditions that exist poleward of 60 degrees S and extend 180 degrees in longitude west from the Mountains of Mitchel (about 30 degrees W), surface frost accumulation is dominated by precipitation. In the opposite hemisphere, the polar atmosphere is relatively warm and clear and frost accumulation is dominated by direct vapour deposition. It is the differences in these deposition styles that determine the cap albedo.

Colaprete, Anthony↗

Simulating Pyrocumulonimbus Clouds Using a Multiscale Wildfire Simulation Framework

Pyrocumulonimbus (pyroCb) clouds, driven by extreme fires under favorable meteorological conditions, can inject smoke into the stratosphere at magnitudes comparable to those of moderate volcanic eruptions, potentially altering the global radiative balance and atmospheric composition. However, simulating pyroCb is particularly challenging in Earth system models. Using the Energy Exascale Earth System Model (E3SM), we developed a novel global multiscale framework to model pyroCb events in California, which includes a high‐resolution fire radiative power time series, a one‐dimensional plume‐rise parameterization, a fire‐induced vertical water vapor transport scheme, and a surface wildfire sensible heat flux representation. Our simulation successfully reproduces many pyroCb features, including cloud height, spatiotemporal evolution, and convective intensity in comparison with satellite and ground‐based observations. Sensitivity experiments show that realistic pyroCb simulation depends on vertical water vapor transport. These advances provide a basis for future exploration of pyroCb impacts at regional and global scales within climate models.

E3SM↗

Improvements to TOVS retrievals over sea ice and applications to estimating Arctic energy fluxes

Modeling studies suggest that polar regions play a major role in modulating the Earth's climate and that they may be more sensitive than lower latitudes to climate change. Until recently, however, data from meteorological stations poleward of 70 degs have been sparse, and consequently, our understanding of air-sea-ice interaction processes is relatively poor. Satellite-borne sensors now offer a promising opportunity to observe polar regions and ultimately to improve parameterizations of energy transfer processes in climate models. This study focuses on the application of the TIROS-N operational vertical sounder (TOVS) to sea-ice-covered regions in the nonmelt season. TOVS radiances are processed with the improved initialization inversion ('3I') algorithm, providng estimates of layer-average temperature and moisture, cloud conditions, and surface characteristics at a horizontal resolution of approximately 100 km x 100 km. Although TOVS has flown continuously on polar-orbiting satellites since 1978, its potential has not been realized in high latitudes because the quality of retrievals is often significantly lower over sea ice and snow than over the surfaces. The recent availability of three Arctic data sets has provided an opportunity to validate TOVS retrievals: the first from the Coordinated Eastern Arctic Experiment (CEAREX) in winter 1988/1989, the second from the LeadEx field program in spring 1992, and the third from Russian drifting ice stations. Comparisons with these data reveal deficiencies in TOVS retrievals over sea ice during the cold season; e.g., ice surface temperature is often 5 to 15 K too warm, microwave emissivity is approximately 15% too low at large view angles, clear/cloudy scenes are sometimes misidentified, and low-level inversions are often not captured. In this study, methods to reduce these errors are investigated. Improvements to the ice surface temperature retrieval have reduced rms errors from approximately 7 K to 3 K; correction of microwaves (50 GHz) brightness temperatures for view angle dependence now allows the surface type (open water versus sea ice) to be determined; modifications to the clear/cloud tests have improved cloud detection over sea ice, especially where it is inhomogeneous; and improved surface temperature estimates have resulted in more successful diagnoses of low-level stratification. Applications of improved retrievals to studies of the Arctic energy budget are encouraging. Preliminary calculations of air-ice stress vectors, 10-m wind speed, and horizontal advection of heat and moisture are presented.

Francis, Jennifer A.↗

Martian B Storm Genesis and Evolution: Initial Analysis of Thermal Datasets.

Introduction: Dust lifting on Mars likely occurs primarily as a result of the exchange of momentum between the atmosphere and the surface via saltation. During saltation, sand-sized particles are mobilized but do not enter into suspension. When these larger particles fall back to the surface, kinetic energy is transferred to smaller dust particles which are then lofted into suspension in the atmosphere. Depending on the altitude to which dust is lofted, it can have a significant effect on atmospheric temperatures. As a strong absorber and emitter in the infrared, dust can influence atmospheric heating and modify the global circulation and weather on Mars [1,2]. Although dust is present in Mars’ atmosphere throughout the year, the atmosphere is generally dustier during the second half of the year when Mars is near perihelion. Observations reveal that episodic global-scale dust storms and fairly regular regional-scale dust storms are superimposed on a well-defined and highly repeatable seasonal cycle of dust opacity and associated mid-level atmospheric temperature responses. Kass et al. (2016) used 50 Pa temperature observations from MRO/MCS to identify three highly repeatable time periods during which regional dust storms occur, and designated them the “A”, “B” and “C” storms. While “A” and “C” storms have been studied a fair amount to-date, “B” storms have not yet been investigated in detail. This study explores the generation and evolution of the annually recurring regional dust storm known as the “B” storm, which was identified and categorized by Kass et al. (2016) based on 25 km (50 Pa) temperature observations. The B storm is a southern-hemisphere (SH) phenomenon that originates at the cap edge just after perihelion and which reaches peak intensity during the SH summer solstice, Ls 270. It may originate from the cap edge storms that spawn near the edge of the seasonal CO2 cap during retreat, but the mechanisms for B storm genesis have yet to be determined definitively [1]. Methods: We will use observational data sets and a global climate model (GCM) to investigate “B” regional storms. The data analysis component will include the analysis of imagery from MGS/MOC and MRO/MARCI, and spectroscopic data sets of dust and temperatures from MGS/TES and MRO/MCS with the goal of fully characterizing the behavior of these storms. Both MGS and TES provide data well-suited for temperature analysis at 25 km. MCS measures atmospheric temperature, dust extinction, and water ice extinction at 5 km intervals from the surface to about 80 km. TES measured atmospheric temperatures, column dust and water ice opacities, and column water vapor abundances. Measurements made by TES extended from the surface to about 40 km [1]. At the 50 Pa (25 km) level, local dust events usually confined to shallower depths are effectively filtered out of the analysis leaving the regional dust events identifiable by their temperature signatures [1]. Our preliminary analysis makes use of the fact that the brightness temperature at 15 microns (T15 temperature) is a close approximation to observed temperature at 25 km. We first reproduce the zonal mean 50 Pa level temperature plots for MY 29-32 to establish a baseline for our procedures moving forward [1]. Expanding on Kass et al. (2016), we include recent MCS data from MY 33 and 34 as well. Preliminary Analysis: The daytime (3PM) T15 temperatures in Figure 1 indicate: in MY 29, a strong A storm at Ls 240, a B storm at high southern latitudes just after Ls 270, and a C storm at Ls 320; in MY 30, a B storm at Ls 270; in MY 31 & MY 32, a B storm just before Ls 270; in MY 33, a B storm at Ls 270; and in MY 34, a strong A storm in the northern hemisphere at Ls 210, and a B storm around Ls 270 although there is a data gap. For the B storms, each is indicative of lofted dust and resultant warming. The daytime temperature structure illustrates that the B storm occurs annually around Ls 270 and is confined to high southern latitudes. It reaches its peak intensity around SH summer solstice, Ls 270, consistently for all six MY assessed. Since direct solar heating is absent overnight, the nighttime T15 temperatures (Figure 2) are often useful for differentiating the heat signature of direct solar heating from the dynamical response to that heating. However, in the southern polar latitudes at perihelion the sun does not set and direct solar heating remains present throughout the night. Importantly for our study, dust lofted in the B storm experiences this direct heating day and night for the entirety of its lifetime. The B storm expands as far north as -60 latitude and decays in latitudinal extent more gradually than it grows. This feature is less obvious in the nighttime (3AM) T15 temperatures (Figure 2). The temperature signal is stronger at night for MY 30-33. The warm pool is larger in area relative to the background at night in these four cases. This more uniform warming masks the “tail” feature somewhat, such that it is barely noticeable during these years. Unfortunately, gaps in MCS data in MY 29 and 34 prevent confirmation of the tail feature during those years, however, the B storm temperature signature follows a very different pattern than that described for MY 30-33. MY 29 and 34 appear to show smaller centers of warming at night and larger centers of warming during the day. This is in opposition to that previously described for MY 30-33. Conclusions and Future Work: We will continue investigating the heat signatures of B storms by looking at the total column heating as recorded by TES. We will also look at lower altitudes for patterns that may describe the relationship between B storms and the cap edge storms that develop while the seasonal cap is retreating. In the future, we will use GCM simulations to determine the atmospheric and thermo-dynamic conditions associated with these storms.

Courtney Marylou Batterson↗

Ice Nucleating Particles, Aerosols and Clouds over the Higher Latitude Southern Ocean

Improved understanding of aerosol-cloud-precipitation interactions is a key goal of the DOE-ASR program. Ice Nucleating Particles (INPs) are rare aerosol particles that trigger ice formation in clouds. By doing so they may initiate precipitation in clouds, and this impacts cloud lifetime. Nowhere may the role of INPs be manifested so dramatically as in the Southern Ocean (SO). This vast region was the focus of the DOE-ARM MARCUS (Measurement of Aerosols, Radiation and CloUds over the Southern Oceans) and MICRE (Macquarie Island Cloud and Radiation Experiment) campaigns. In the SO, a deficit of INPs has been a leading hypothesis for why liquid clouds persist, which underlays the bias of global climate models in predicting excess shortwave radiation reaching the ocean surface between 55°S and Antarctica. In this study, we used the 6-month acquisition of INP data on four MARCUS cruises from Hobart, Tasmania, to Antarctica, and the single-location full annual INP cycle observed in MICRE, to gain a holistic, quantitative picture of the INP number concentrations as a function of temperature and their variability over SO latitudes from 43°S to Antarctica. Augmenting initial processing of filter collections of aerosol particles during MARCUS and MICRE, additional freezing studies following thermal treatment (to removes INPs associated with microbes) and peroxide treatment (to remove all organics), as well as ionic chemistry and total organic carbon analysis, were applied to samples to improve time and space resolution, and differentiate marine from terrestrial contributions. We also performed Next Generation Sequencing to determine that bacterial composition can tag periods of enhanced and degraded marine organic INPs and the relative lack of occurrence of land-sourced bio-particles. Through these and other analyses, INP data were categorized into representative source types (e.g., marine versus terrestrially-influenced). The products of our analyses are being used to parameterize ice formation for use in numerical modeling studies to determine if, and when, ocean-derived INPs control the microphysical composition and radiative balance of SO clouds in a manner not presently captured by global models. The specific objectives and approaches proposed were largely accomplished, including: 1) Additional and value-added chemical and biological analyses of archived samples of aerosols were completed to categorize and quantify INP types and concentrations over the SO. 2) Analyses were completed to place INP data in meteorological and aerosol context. 3) Sea spray aerosol INPs were demonstrated to dominate the SO region in all but episodic events. 4) New parameterizations for INP sources relevant to the SO were constructed. 5) Collaborative modeling studies demonstrating the crucial role of INPs in determining cloud radiative and precipitation properties were conducted with MARCUS and MICRE partners. Collaborative publication submissions in this regard ensued within the two year study. This work will advance the science of interactions of aerosols, clouds and precipitation, to improve their representation in regional and global climate models. Results have informed representation of primary ice crystal nucleation, give inference to the role of secondary processes, and improve investigations of aerosol influences on clouds via ice nucleation over SO high latitudes and similar vast ocean regions. Application will improve representation of such interactions for clouds in both regional and global climate models. The data base and analyses methods applied will continue to serve research studies in the future.

58 GEOSCIENCES↗

Climate Change Impact Uncertainties for Maize in Panama: Farm Information, Climate Projections, and Yield Sensitivities

We present results from a pilot project to characterize and bound multi-disciplinary uncertainties around the assessment of maize (Zea mays) production impacts using the CERES-Maize crop model in a climate-sensitive region with a variety of farming systems (Panama). Segunda coa (autumn) maize yield in Panama currently suffers occasionally from high water stress at the end of the growing season, however under future climate conditions warmer temperatures accelerate crop maturation and elevated CO (sub 2) concentrations improve water retention. This combination reduces end-of-season water stresses and eventually leads to small mean yield gains according to median projections, although accelerated maturation reduces yields in seasons with low water stresses. Calibrations of cultivar traits, soil profile, and fertilizer amounts are most important for representing baseline yields, however sensitivity to all management factors is reduced in an assessment of future yield changes (most dramatically for fertilizers), suggesting that yield changes may be more generalizable than absolute yields. Uncertainty around General Circulation Model (GCM)s' projected changes in rainfall gain in importance throughout the century, with yield changes strongly correlated with growing season rainfall totals. Climate changes are expected to be obscured by the large inter-annual variations in Panamanian climate that will continue to be the dominant influence on seasonal maize yield into the coming decades. The relatively high (A2) and low (B1) emissions scenarios show little difference in their impact on future maize yields until the end of the century. Uncertainties related to the sensitivity of CERES-Maize to carbon dioxide concentrations have a substantial influence on projected changes, and remain a significant obstacle to climate change impacts assessment. Finally, an investigation into the potential of simple statistical yield emulators based upon key climate variables characterizes the important uncertainties behind the selection of climate change metrics and their performance against more complex process-based crop model simulations, revealing a danger in relying only on long-term mean quantities for crop impact assessment.

Maize↗

Multi-year global climatic effects of atmospheric dust from large bolide impacts

The global climatic effects of dust generated by the impact of a 10 km-diameter bolide was simulated using a one-dimensional (vertical only) globally-averaged climate model by Pollack et al. The goal of the simulation is to examine the regional climate effects, including the possibility of coastal refugia, generated by a global dust cloud in a model having realistic geographic resolution. The climate model assumes the instantaneous appearance of a global stratospheric dust cloud with initial optical depth of 10,000. The time history of optical depth decreases according to the detailed calculations of Pollack et al., reaching an optical depth of unity at day 160, and subsequently decreasing with an e-folding time of 1 year. The simulation is carried out for three years in order to examine the atmospheric effects and recovery over several seasons. The simulation does not include any effects of NOx, CO2, or wildfire smoke injections that may accompany the creation of the dust cloud. The global distribution of surface temperature changes, freezing events, precipitation and soil moisture effects and sea ice increases will be discussed.

Thompson, Starley L.↗

Climate models show colorado drying sooner and with greater certainty east of the continental divide

Many studies have examined the aridity of the Colorado River Basin and the possible impacts of climate change which could further strain already over-allocated water resources in the region. Fewer studies have examined the multiple Colorado Rocky Mountain headwater regions specifically. This is especially true of areas East of the Continental Divide, despite water originating there being critical to cities and agriculture in Eastern Colorado and further downstream. This paper explores and compares drying trends in the Eastern and Western Colorado Rocky Mountains using single-model initial-condition large ensembles from ten global climate models. The use of multiple models allows us to identify signals that are consistent across different physics parameterizations, model grids, and other model intricacies. The large ensembles also allow us to quantify the time of emergence of these climate change signals--that is, when did (or when will) the long term change due to anthropogenic greenhouse gasses exceed the internal variability of the climate system. Consistent with previous studies, we find evidence of drying on both sides of the Continental Divide. That drying is more pronounced, occurs sooner, and is more consistent across global climate models in the East, however, highlighting the region’s importance despite generally receiving less attention than the West.

Rugg, Allyson↗

TPSAS-NF1676L-33231-DND

It is well established that clouds have a profound influence on Earth's energy budget and that cloud feedbacks are responsible for much of the uncertainty in climate model projections of global warming in response to radiative forcing by greenhouse gases. While most model evaluation efforts focus on model representation of the observed mean state. For example, the regional mean climatology of key climate variables there is also tremendous value in testing how a model represents observed internal variations of the climate system. Here we use CERES observations during and after the so-called global warming hiatus to evaluate a subset of CMIP6 climate models. The CERES data show a marked 0.83 Wm-2 reduction in global mean reflected SW TOA flux during the three years following the hiatus that results in an increase in net energy into the climate system. The primary driver of the decrease in SW TOA flux is a reduction in low cloud cover over the eastern Pacific Ocean that occurs in response to an unprecedented increase in SSTs in that region following the hiatus. The CMIP6 simulations consist of AMIP-style experiments in which SSTs and sea-ice boundary conditions are constrained by observations. For each model, the AMIP runs were extended through the end of 2017 from the official CMIP6 AMIP end period in 2014. Results show that the models do a good job at capturing the pattern of observed changes in TOA flux observed by CERES, but tend to underestimate the magnitude.

Norman G Loeb↗

Improvement in Simulation of Eurasian Winter Climate Variability with a Realistic Arctic Sea Ice Condition in an Atmospheric GCM

The present study investigates how much a realistic Arctic sea ice condition can contribute to improve simulation of the winter climate variation over the Eurasia region. Model experiments are set up using different sea ice boundary conditions over the past 24 years (i.e., 1988-2011). One is an atmospheric model inter-comparison (AMIP) type of run forced with observed sea-surface temperature (SST), sea ice, and greenhouse gases (referred to as Exp RSI), and the other is the same as Exp RSI except for the sea ice forcing, which is a repeating climatological annual cycle (referred to as Exp CSI). Results show that Exp RSI produces the observed dominant pattern of Eurasian winter temperatures and their interannual variation better than Exp CSI (correlation difference up to approx. 0.3). Exp RSI captures the observed strong relationship between the sea ice concentration near the Barents and Kara seas and the temperature anomaly across Eurasia, including northeastern Asia, which is not well captured in Exp CSI. Lagged atmospheric responses to sea ice retreat are examined using observations to understand atmospheric processes for the Eurasian cooling response including the Arctic temperature increase, sea-level pressure increase, upper-level jet weakening and cold air outbreak toward the mid-latitude. The reproducibility of these lagged responses by Exp RSI is also evaluated.

sea-ice↗

Satellite Perspective of Aerosol Intercontinental Transport: From Qualitative Tracking to Quantitative Characterization

Evidence of aerosol intercontinental transport (ICT) is both widespread and compelling. Model simulations suggest that ICT could significantly affect regional air quality and climate, but the broad inter-model spread of results underscores a need of constraining model simulations with measurements. Satellites have inherent advantages over in situ measurements to characterize aerosol ICT, because of their spatial and temporal coverage. Significant progress in satellite remote sensing of aerosol properties during the Earth Observing System (EOS) era offers opportunity to increase quantitative characterization and estimates of aerosol ICT, beyond the capability of pre-EOS era satellites that could only qualitatively track aerosol plumes. EOS satellites also observe emission strengths and injection heights of some aerosols, aerosol precursors, and aerosol-related gases, which can help characterize aerosol ICT. After an overview of these advances, we review how the current generation of satellite measurements have been used to (1) characterize the evolution of aerosol plumes (e.g., both horizontal and vertical transport, and properties) on an episodic basis, (2) understand the seasonal and inter-annual variations of aerosol ICT and their control factors, (3) estimate the export and import fluxes of aerosols, and (4) evaluate and constrain model simulations. Substantial effort is needed to further explore an integrated approach using measurements from on-orbit satellites (e.g., A-Train synergy) for observational characterization and model constraint of aerosol intercontinental transport and to develop advanced sensors for future missions.

Yu, Hongbin↗