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At least 1,315 records · Page 73

Sea Level Rise in the CESM Large Ensemble: The Role of Individual Climate Forcings and Consequences for the Coming Decades

The emergence of a spatial pattern in the externally forced response (FR) of dynamic sea level (DSL) during the altimeter era has recently been demonstrated using climate models but our understanding of its initial emergence, drivers, and implications for the future is poor. Here the anthropogenic forcings of the DSL pattern are explored using the Community Earth System Model Large Ensemble (CESM-LE) and Single-Forcing Large Ensemble, a newly available set of simulations where values of individual forcing agents remain fixed at 1920 levels, allowing for an estimation of their effects. Statistically significant contributions to the DSL FR are identified for greenhouse gases (GHGs) and industrial aerosols (AERs), with particularly strong contributions resulting from AERs in the mid-twentieth century and GHGs in the late twentieth and twenty-first century. Secondary, but important, contributions are identified for biomass burning aerosols in the equatorial Atlantic Ocean in the mid-twentieth century, and for stratospheric ozone in the Southern Ocean during the late twentieth century. Key to understanding regional DSL patterns are ocean heat content and salinity anomalies, which are driven by surface heat and freshwater fluxes, ocean dynamics, and the spatial structure of seawater thermal expansivity. Finally, potential implications for the interpretation of DSL during the satellite era and the longer records from tide gauges are suggested as a topic for future research.

54 ENVIRONMENTAL SCIENCES↗

Separating the Influences of Low-Latitude Warming and Sea Ice Loss on Northern Hemisphere Climate Change

Abstract Analyzing a multimodel ensemble of coupled climate model simulations forced with Arctic sea ice loss using a two-parameter pattern-scaling technique to remove the cross-coupling between low- and high-latitude responses, the sensitivity to high-latitude sea ice loss is isolated and contrasted to the sensitivity to low-latitude warming. Despite some differences in experimental design, the Northern Hemisphere near-surface atmospheric sensitivity to sea ice loss is found to be robust across models in the cold season; however, a larger intermodel spread is found at the surface in boreal summer, and in the free tropospheric circulation. In contrast, the sensitivity to low-latitude warming is most robust in the free troposphere and in the warm season, with more intermodel spread in the surface ocean and surface heat flux over the Northern Hemisphere. The robust signals associated with sea ice loss include upward turbulent and longwave heat fluxes where sea ice is lost, warming and freshening of the Arctic Ocean, warming of the eastern North Pacific Ocean relative to the western North Pacific with upward turbulent heat fluxes in the Kuroshio Extension, and salinification of the shallow shelf seas of the Arctic Ocean alongside freshening in the subpolar North Atlantic Ocean. In contrast, the robust signals associated with low-latitude warming include intensified ocean warming and upward latent heat fluxes near the western boundary currents, freshening of the Pacific Ocean, salinification of the North Atlantic, and downward sensible and longwave fluxes over the ocean.

54 ENVIRONMENTAL SCIENCES↗

Examining future changes in coastal low-level jet properties offshore California through dynamical downscaling

The coastal low-level jet, or coastal low-level jet (CLLJ), is a synoptically-forced meteorological feature frequently present offshore the western United States (U.S.). Characterized by a wind speed maximum that resides at the top of the marine boundary layer, the CLLJ is largely controlled by the location and strength of the North Pacific High (NPH) as well as the coastal geometry. Considering the rich wind resource available in this offshore region, the Bureau of Ocean Energy Management identified wind energy lease areas offshore California and supported the deployment of two U.S. Department of Energy wind lidar buoys near Morro Bay and Humboldt. Despite our relatively good understanding of the fundamental mechanisms responsible for large-scale CLLJ properties offshore the western U.S., future changes in CLLJ characteristics are less clear. To address this research challenge, and ultimately to better inform future wind turbine deployments, we use simulations driven by three global climate models (GCMs). We apply self-organizing maps to the model outputs for a historical and two future climate periods to show the range of NPH regimes that support CLLJ conditions during the warm seasons, as well as the subtle contribution from land-falling cyclones approaching the mainland during the cold seasons. Compared to the historical period, the three GCM-driven simulations agree that CLLJ conditions will become more (less) prevalent from central California northward (southward). They agree less with respect to future changes in maximum CLLJ wind speeds and CLLJ heights. However, after considering model biases present during the historical period, wind speeds between the models are actually more similar during the 2070–2095 period than during the historical period. The potential combination of more frequent CLLJ conditions characterized by relatively consistent wind speeds occurring at lower heights across northern California suggests that the Humboldt lease area may be ideal for a long-term wind turbine deployment.

54 ENVIRONMENTAL SCIENCES↗

Investigation of short-term effective radiative forcing of fire aerosols over North America using nudged hindcast ensembles

Aerosols from fire emissions can potentially have large impact on clouds and radiation. However, fire aerosol sources are often intermittent, and their effect on weather and climate is difficult to quantify. Here we investigated the short-term effective radiative forcing of fire aerosols using the global aerosol–climate model Community Atmosphere Model version 5 (CAM5). Different from previous studies, we used nudged hindcast ensembles to quantify the forcing uncertainty due to the chaotic response to small perturbations in the atmosphere state. Daily mean emissions from three fire inventories were used to consider the uncertainty in emission strength and injection heights. The simulated aerosol optical depth (AOD) and mass concentrations were evaluated against in situ measurements and reanalysis data. Overall, the results show the model has reasonably good predicting skills. Short (10-day) nudged ensemble simulations were then performed with and without fire emissions to estimate the effective radiative forcing. Results show fire aerosols have large effects on both liquid and ice clouds over the two selected regions in April 2009. Ensemble mean results show strong negative shortwave cloud radiative effect (SCRE) over almost the entirety of southern Mexico, with a 10-day regional mean value of −3.0 W m −2 . Over the central US, the SCRE is positive in the north but negative in the south, and the regional mean SCRE is small (−0.56 W m −2 ). For the 10-day average, we found a large ensemble spread of regional mean shortwave cloud radiative effect over southern Mexico (15.6 % of the corresponding ensemble mean) and the central US (64.3 %), despite the regional mean AOD time series being almost indistinguishable during the 10-day period. Moreover, the ensemble spread is much larger when using daily averages instead of 10-day averages. This demonstrates the importance of using a large ensemble of simulations to estimate the short-term aerosol effective radiative forcing.

54 ENVIRONMENTAL SCIENCES↗

The implications of uncertain renewable resource potentials for global wind and solar electricity projections

Studies exploring long-term energy system transitions rely on resource cost-supply curves derived from estimates of renewable energy (RE) potentials to generate wind and solar power projections. However, estimates of RE potentials are characterized by large uncertainties stemming from methodological assumptions that vary across studies, including factors such as the suitability of land and the performance and configuration of technology. Based on a synthesis of modeling approaches and parameter values used in prior studies, we explore the implications of these uncertain assumptions for onshore wind and solar photovoltaic electricity generation projections globally using the Global Change Analysis Model. We show that variability in parametric assumptions related to land use (e.g. land suitability) are responsible for the most substantial uncertainty in both wind and solar generation projections. Additionally, assumptions about the average turbine installation density and turbine technology are responsible for substantial uncertainty in wind generation projections. Under scenarios that account for climate impacts on wind and solar energy, we find that these parametric uncertainties are far more significant than those emerging from differences in climate models and scenarios in a global assessment, but uncertainty surrounding climate impacts (across models and scenarios) have significant effects regionally, especially for wind. Our analysis suggests the need for studies focusing on long-term energy system transitions to account for this uncertainty.

14 SOLAR ENERGY↗

Evaluation of tropical channel refinement using MPAS‐A aquaplanet simulations

Abstract Climate models with variable‐resolution grids offer a computationally less expensive way to provide more detailed information and increased accuracy by resolving processes that cannot be adequately represented by a coarser grid. This study uses the Model for Prediction Across Scales‐Atmosphere (MPAS‐A), consisting of a nonhydrostatic dynamical core and a subset of Weather Research and Forecasting (WRF) model physics, to investigate the potential benefits of using tropical channel refinement. The simulations are performed with an idealized aquaplanet configuration using 30 and 240 km global grid spacing, and two variable‐resolution grids spanning the same grid spacing range; one with a narrow (20°S–20°N) and one with a wide (30°S–30°N) tropical channel refinement. Increasing resolution in the tropics impacts both the tropical and extratropical circulation. Compared to the 30 km global grid, both refined channel simulations exhibit slightly stronger updrafts inside the Hadley cell resulting in more resolved precipitation. Using a wider tropical refinement leads to a closer correspondence with the global high‐resolution grid. While different grid spacings produce similar cloud size distributions that are consistent with observations, the dependence of precipitation rate on cloud size varies among simulations. The refined channel simulations show improved tropical and extratropical precipitation relative to the global coarse simulation. All simulations show a single precipitation peak centered on the equator. Although the results show that tropical refinement is an effective method for avoiding artifacts due to grid resolution sensitivities seen in earlier studies that only refined a portion of the tropics, some biases remain well inside of the refinement region.

54 ENVIRONMENTAL SCIENCES↗

The Diurnal Cycle of the Boundary Layer, Convection, Clouds, and Surface Radiation in a Coastal Monsoon Environment (Darwin Australia)

The diurnal variation of convection and associated cloud and radiative properties remains a significant issue in global NWP and climate models. This study analyzes observed diurnal variability of convection in a coastal monsoonal environment examining the interaction of convective rain clouds, their associated cloud properties, and the impact on the surface radiation and corresponding boundary layer structure during periods where convection is suppressed or active on the large scale. The analysis uses data from the Tropical Warm Pool International Cloud Experiment (TWP-ICE) as well as routine measurements from the Australian Bureau of Meteorology and the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program. Both active monsoonal and large-scale suppressed (buildup and break) conditions are examined and demonstrate that the diurnal variation of rainfall is much larger during the break periods and the spatial distribution of rainfall is very different between the monsoon and break regimes. During the active monsoon the total net radiative input to the surface is decreased by more than 3 times the amount than during the break regime - this total radiative cloud forcing is found to be dominated by the shortwave (SW) cloud effects because of the much larger optical thicknesses and persistence of long-lasting anvils and cirrus cloud decks associated with the monsoon regime. These differences in monsoon versus break surface radiative energy contribute to low-level air temperature differences in the boundary layer over the land surfaces.

54 ENVIRONMENTAL SCIENCES↗

CICE, The Los Alamos Sea Ice Model

The Los Alamos sea ice model (CICE) is the result of an effort to develop a computationally efficient sea ice component for a fully coupled atmosphere–land–ocean–ice global climate model. It was originally designed to be compatible with the Parallel Ocean Program (POP), an ocean circulation model developed at Los Alamos National Laboratory for use on massively parallel computers. CICE has several interacting components: a vertical thermodynamic model that computes local growth rates of snow and ice due to vertical conductive, radiative and turbulent fluxes, along with snowfall; an elastic-viscous-plastic model of ice dynamics, which predicts the velocity field of the ice pack based on a model of the material strength of the ice; an incremental remapping transport model that describes horizontal advection of the areal concentration, ice and snow volume and other state variables; and a ridging parameterization that transfers ice among thickness categories based on energetic balances and rates of strain. It also includes a biogeochemical model that describes evolution of the ice ecosystem. The CICE sea ice model is used for climate research as one component of complex global earth system models that include atmosphere, land, ocean and biogeochemistry components. It is also used for operational sea ice forecasting in the polar regions and in numerical weather prediction models.

Hunke, Elizabeth↗

CICE, The Los Alamos Sea Ice Model

The Los Alamos sea ice model (CICE) is the result of an effort to develop a computationally efficient sea ice component for a fully coupled atmosphere–land–ocean–ice global climate model. It was originally designed to be compatible with the Parallel Ocean Program (POP), an ocean circulation model developed at Los Alamos National Laboratory for use on massively parallel computers. CICE has several interacting components: a vertical thermodynamic model that computes local growth rates of snow and ice due to vertical conductive, radiative and turbulent fluxes, along with snowfall; an elastic-viscous-plastic model of ice dynamics, which predicts the velocity field of the ice pack based on a model of the material strength of the ice; an incremental remapping transport model that describes horizontal advection of the areal concentration, ice and snow volume and other state variables; and a ridging parameterization that transfers ice among thickness categories based on energetic balances and rates of strain. It also includes a biogeochemical model that describes evolution of the ice ecosystem. The CICE sea ice model is used for climate research as one component of complex global earth system models that include atmosphere, land, ocean and biogeochemistry components. It is also used for operational sea ice forecasting in the polar regions and in numerical weather prediction models.

Hunke, Elizabeth↗

The Mechanisms of the Atlantic Meridional Overturning Circulation Slowdown Induced by Arctic Sea Ice Decline

In this work, we explore the mechanisms by which Arctic sea ice decline affects the Atlantic meridional overturning circulation (AMOC) in a suite of numerical experiments perturbing the Arctic sea ice radiative budget within a fully coupled climate model. The imposed perturbations act to increase the amount of heat available to melt ice, leading to a rapid Arctic sea ice retreat within 5 years after the perturbations are activated. In response, the AMOC gradually weakens over the next ~100 years. The AMOC changes can be explained by the accumulation in the Arctic and subsequent downstream propagation to the North Atlantic of buoyancy anomalies controlled by temperature and salinity. Initially, during the first decade or so, the Arctic sea ice loss results in anomalous positive heat and salinity fluxes in the subpolar North Atlantic, inducing positive temperature and salinity anomalies over the regions of oceanic deep convection. At first, these anomalies largely compensate one another, leading to a minimal change in upper ocean density and deep convection in the North Atlantic. Over the following years, however, more anomalous warm water accumulates in the Arctic and spreads to the North Atlantic. At the same time, freshwater that accumulates from seasonal sea ice melting over most of the upper Arctic Ocean also spreads southward, reaching as far as south of Iceland. These warm and fresh anomalies reduce upper ocean density and suppress oceanic deep convection. The thermal and haline contributions to these buoyancy anomalies, and therefore to the AMOC slowdown during this period, are found to have similar magnitudes. We also find that the related changes in horizontal wind-driven circulation could potentially push freshwater away from the deep convection areas and hence strengthen the AMOC, but this effect is overwhelmed by mean advection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Robust Anthropogenic Signal Identified in the Seasonal Cycle of Tropospheric Temperature

Previous work identified an anthropogenic fingerprint pattern in T AC (x, t), the amplitude of the seasonal cycle of mid- to upper-tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite T AC (x, t) data could have been influenced by real-world multidecadal internal variability (MIV). Here we address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their T AC (x, t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3–4) intermodel and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal T AC (x, t) variability associated with the Atlantic multidecadal oscillation and El Niño–Southern Oscillation. The robustness of the seasonal cycle detection and attribution results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced T AC (x, t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.

54 ENVIRONMENTAL SCIENCES↗

Improved atmosphere-ocean coupled modeling in the tropics for climate prediction

We investigated the initial development of the double ITCZ in the Community Climate System Model (CCSM Version 3) in the central Pacific. Starting from a resting initial condition of the ocean in January, the model developed a warm bias of sea-surface temperature (SST) in the central Pacific from 5oS to 10oS in the first three months. We found this initial bias to be caused by excessive surface shortwave radiation that is also present in the standalone atmospheric model. The initial bias is further amplified by biases in both surface latent heat flux and horizontal heat transport in the upper ocean. These biases are caused by the responses of surface winds to SST bias and the thermocline structure to surface wind curls. We also showed that the warming biases in surface solar radiation and latent heat fluxes are seasonally offset by cooling biases from reduced solar radiation after the austral summer due to cloud responses and in the austral fall due to enhanced evaporation when the maximum SST is closest to the equator. The warming biases from the dynamic heat transport by ocean currents however stay throughout all seasons once they are developed, which are eventually balanced by enhanced energy exchange and penetration of solar radiation below the mixed layer. Our results also showed that the equatorial cold tongue develops after the warm biases in the south central Pacific, and the overestimation of surface shortwave radiation recurs in the austral summer in each year.

54 ENVIRONMENTAL SCIENCES↗

Extratropical Cloud Feedback Constrained by Cloud Sources and Sinks in Cyclones

Constraining cloud feedback in global climate models (GCMs) using observations is important for establishing accurate predictions of future climate. Uncertainty in shortwave cloud feedback (SW FB ) dominates uncertainty in total cloud feedback. Recent studies show a shift toward more positive extratropical SW FB in the latest generations of GCMs leading to the emergence of very high equilibrium climate sensitivity (ECS). In this study, we use precipitation efficiency and albedo susceptibility to constrain liquid water path (LWP) response to warming and SW FB in the Southern Ocean (SO; 50°–80°S). We analyze precipitation in extratropical cyclones (ECs) to learn about extratropical condensed water sink processes, combined with observations of clouds and moisture convergence, and use the analysis to better understand and constrain SW FB . We utilize a perturbed parameter ensemble (PPE) hosted in the Community Atmosphere Model, version 6 (CAM6), to provide a constraint on SW FB based on observations from Clouds and the Earth’s Radiant Energy System (CERES) and Multisensor Advanced Climatology of LWP (MAC-LWP). We apply Gaussian process regression to emulate the model response to all parameters perturbed in the PPE. Confronting the emulator output with observations provides a new estimated response of Earth to global warming. Furthermore, our new estimates of SO LWP reduce the PPE range by 66%–72%, which results in a shortwave cloud radiative effect estimated range that is 27%–34% less than the PPE range. Observations suggest a more positive SO SW FB than the Community Earth System Model, version 2 (CESM2), and consequently do not reject the high climate sensitivity GCMs emerging from the Coupled Model Intercomparison Project phase 6 (CMIP6).

Atmosphere↗

Comparison of Dynamically and Statistically Downscaled Seasonal Climate Forecasts for the Cold Season over the United States

This study compares two approaches, dynamical and statistical downscaling, for their potential to improve regional seasonal forecasts for the United States (U.S.) during the cold season. In the MultiRCM Ensemble Downscaling (MRED) project, seven regional climate models (RCMs) are used to dynamically downscale the Climate Forecast System (CFS) seasonal prediction over the conterminous U.S. out to 5 months for the period of 1982–2003. The simulations cover December to April of next year with 10 ensemble members from each RCM with different initial and boundary conditions from the corresponding ensemble members. These dynamically downscaled forecasts are compared with statistically downscaled forecasts produced by two bias correction methods applied to both the CFS and RCM forecasts. Results of the comparison suggest that the RCMs add value in seasonal prediction application, but the improvements largely depend on location, forecast lead time, variables, and skill metrics used for evaluation. Generally, more improvements are found over the Northwest and North Central U.S. for the shorter lead times. The comparison results also suggest a hybrid forecast system that combines both dynamical and statistical downscaling methods have the potential to maximize prediction skill.

Comparison, cold season, downscaled, seasonal, cli↗

Projected Changes to Cool‐Season Storm Tides in the 21st Century Along the Northeastern United States Coast

Abstract This study investigates changes and uncertainties to cool‐season (November‐March) storm tides along the U.S. northeast coast in the 21st century under the high RCP8.5 emission scenario compared to late 20th century. A high‐fidelity (50‐m coastal resolution) hydrodynamic storm tide model is forced with three dynamically downscaled regional climate models (RCMs) over three decadal periods (historical, mid‐21st century, and late‐21st century) to project future changes in peak storm tide elevations at coastal counties in the region. While there is no absolute consensus on future changes to storm tides, for any one future decade two out of the three RCMs project an increase at counties along the Hudson River, Delaware River, and northern Chesapeake Bay due to more intense cyclones that track inland of these locations leading to favorable surge generating conditions. The same RCMs also project a decrease at counties facing the open ocean in the mid‐Atlantic Bight as cyclone densities just offshore of the coastline decrease, particularly by late‐century. The greater tidal range in northern areas of the region leads to uncertainty in the magnitude and sign of the projected changes to storm tide levels due to the arbitrary timing of the local tidal stage and the surge‐producing weather event. This random tide‐weather timing is less important in the Chesapeake Bay and unimportant in Albemarle Sound and Pamlico Sound. Similar to other recent studies, we highlight that sea level rise is likely to be more critical than storm climatology for future changes to the cool‐season coastal flooding potential.

54 ENVIRONMENTAL SCIENCES↗

Evaluating E3SM Global Storm‐Resolving Model Simulations of Deep Convection: Insights From DP‐SCREAM During TRACER

Global Storm-Resolving Models (GSRMs) are becoming increasingly vital for advancing climate modeling and improving the prediction of extreme weather events. Houston, a coastal region frequently affected by deep convective storms, offers an ideal setting to evaluate the ability of GSRMs to simulate deep convection. This study assesses the performance of the Doubly Periodic Simple Cloud-Resolving E3SM (Energy Exascale Earth System Model) Atmosphere Model (DP-SCREAM) using observations from the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. DP-SCREAM effectively reproduces the diurnal cycles of clouds and precipitation, demonstrating much greater skill than the E3SM single column model. The DP-SCREAM is demonstrated to be applicable to coastal regions, partially due to the forcing data sets already capturing the influence of breezes. DP-SCREAM also replicates biases persistent in the global version of SCREAM: the underrepresentation of boundary layer shallow clouds, a lack of mid-level congestus clouds, and the popcorn convection, characterized by small and disorganized convective cells generating the strongest precipitation. To investigate these issues, two sensitivity experiments were conducted: increasing the mixing length and scaling up the buoyancy flux within the Simplified Higher Order Closure scheme. Increasing the mixing length improved mid-level congestus representation and reduced unrealistic early morning fog occurrence. Enhancing buoyancy flux only marginally improved the bias of underproduced big convective cells. In conclusion, an additional resolution sensitivity test at 0.5 km grid spacing demonstrated that a refined horizontal resolution alone is insufficient to resolve these biases.

54 ENVIRONMENTAL SCIENCES↗

Final Technical Report for Collaborative Research: Developing and Implementing Ocean-Atmosphere Reanalyses for Climate Applications (OARCA)

As an important step toward a coupled data assimilation system for generating reanalysis fields needed to assess climate model projections, the Ocean Atmosphere Coupled Reanalysis for Climate Applications (OARCA) project assesses and improves the longest reanalyses currently available of the atmosphere and ocean: the 20th Century Reanalysis Project (20CR) and the Simple Ocean Data Assimilation with sparse observational input (SODAsi) system, respectively. In this project, we make off-line but coordinated improvements in the 20CR and SODAsi datasets, with improvements in one feeding into improvements of the other through an iterative generation of new versions. These datasets now span from the 19th to 21st centuries. We then study the extreme weather and variability from days to decades of the resulting datasets. A total of 24 publications have been produced in this project.

54 ENVIRONMENTAL SCIENCES↗