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James R. Ledwell

Abstract

Lightening of bottom water is required to close the abyssal overturning circulation, believed to play an important role in the climate system. A tracer release experiment and turbulence measurement programs have revealed how bottom water is lightened, and illuminated the associated circulation in the deep Brazil Basin, a representative region of the global ocean. Tracer was released on an isopycnal surface about 4000 m deep, over one of the fracture zones emanating from the Mid-Atlantic Ridge (MAR). Tracer that mixed toward the bottom moved toward the MAR across isopycnal surfaces that bend down to intersect the bottom at a rate implying a near-bottom buoyancy flux of 1.5 × 10−9 m2 s−3, somewhat larger than inferred from dissipation measurements. The diffusivity at the level of the tracer release is estimated at 4.4 ± 1 × 10−4 m2 s−1, again larger than inferred from dissipation rates. The main patch moved southwest at about 2 cm s−1 while sinking due to the divergence of buoyancy flux above the bottom layer. The isopycnal eddy diffusivity was about 100 m2 s−1. Westward flow away from the MAR is the return flow balancing the eastward near-bottom upslope flow. The southward component of the flow is roughly consistent with conservation of potential vorticity. The circulation as well as the pattern of diapycnal flux are qualitatively the same as in but are more robust. The results indicate that diapycnal diffusivity is about twice that invoked by in calculating the basinwide buoyancy budget.

Significance Statement

Buoyancy flux into the abyssal waters is required to close the overturning circulation of those waters, an important part of the climate system. This contribution presents a robust view of the strength of that buoyancy flux and the associated circulation.

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Clark Weaver
,
Dong L. Wu
,
P. K. Bhartia
,
Gordon Labow
,
David P. Haffner
,
Lauren Borgia
,
Laura McBride
, and
Ross Salawitch

Abstract

We construct a long-term record of top of atmosphere (TOA) shortwave (SW) albedo of clouds and aerosols from 340-nm radiances observed by NASA and NOAA satellite instruments from 1980 to 2013. We compare our SW cloud+aerosol albedo with simulated cloud albedo from both AMIP and historical CMIP6 simulations from 47 climate models. While most historical runs did not simulate our observed spatial pattern of the trends in albedo over the Pacific Ocean, four models qualitatively simulate our observed patterns. Those historical models and the AMIP models collectively estimate an equilibrium climate sensitivity (ECS) of ∼3.5°C, with an uncertainty from 2.7° to 5.1°C. Our ECS estimates are sensitive to the instrument calibration, which drives the wide range in ECS uncertainty. We use instrument calibrations that assume a neutral change in reflectivity over the Antarctic ice sheet. Our observations show increasing cloudiness over the eastern equatorial Pacific and off the coast of Peru as well as neutral cloud trends off the coast of Namibia and California. To produce our SW cloud+aerosol albedo, we first retrieve a black-sky cloud albedo (BCA) and empirically correct the sampling bias from diurnal variations. Then, we estimate the broadband proxy albedo using multiple nonlinear regression along with several years of CERES cloud albedo to obtain the regression coefficients. We validate our product against CERES data from the years not used in the regression. Zonal mean trends of our SW cloud+aerosol albedo show reasonable agreement with CERES as well as the Pathfinder Atmospheres–Extended (PATMOS-x) observational dataset.

Significance Statement

Equilibrium climate sensitivity is a measure of the rise in global temperature over hundreds of years after a doubling of atmospheric CO2 concentration. Current state-of-the-art climate models forecast a wide range of equilibrium climate sensitivities (1.5°–6°C), due mainly to how clouds, aerosols, and sea surface temperatures are simulated within these models. Using data from NASA and NOAA satellite instruments from 1980 to 2013, we first construct a dataset that describes how much sunlight has been reflected by clouds over the 34 years and then we compare this data record to output from 47 climate models. Based on these comparisons, we conclude the best estimate of equilibrium climate sensitivity is about 3.5°C, with an uncertainty range of 2.7°–5.1°C.

Open access
Yuanyuan Song
,
Yuanlong Li
,
Aixue Hu
,
Lijing Cheng
,
Gaël Forget
,
Xiaodan Chen
,
Jing Duan
, and
Fan Wang

Abstract

As the major sink of anthropogenic heat, the Southern Ocean has shown quasi-symmetric, deep-reaching warming since the mid-twentieth century. In comparison, the shorter-term heat storage pattern of the Southern Ocean is more complex and has notable impacts on regional climate and marine ecosystems. By analyzing observational datasets and climate model simulations, this study reveals that the Southern Ocean exhibits prominent decadal (>8 years) variability extending to ∼700-m depth and is characterized by out-of-phase changes in the Pacific and Atlantic–Indian Ocean sectors. Changes in the Pacific sector are larger in magnitude than those in the Atlantic–Indian Ocean sectors and dominate the total heat storage of the Southern Ocean on decadal time scales. Instead of heat uptake through surface heat fluxes, these asymmetric variations arise primarily from wind-driven heat redistribution. Pacemaker and preindustrial simulations of the Community Earth System Model version 1 (CESM1) suggest that these variations in Southern Ocean winds arise primarily from natural variability of the tropical Pacific, as represented by the interdecadal Pacific oscillation (IPO). Through atmospheric teleconnection, the positive phase of the IPO gives rise to higher-than-normal sea level pressure and anticyclonic wind anomalies in the 50°–70°S band of the Pacific sector. These winds lead to warming of 0–700 m by driving the convergence of warm water. The opposite processes, involving cyclonic winds and upper-layer divergence, occur in the Atlantic–Indian Ocean sector. These findings aid our understanding of the time-varying heat storage of the Southern Ocean and provide useful implications on initialized decadal climate prediction.

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Brian H. Tang
,
Rosimar Rios-Berrios
, and
Jun A. Zhang

Abstract

This study presents a method to diagnose radial ventilation, the horizontal flux of relatively low-θ e air into tropical cyclones, from dropsonde observations. We used this method to investigate ventilation changes over three consecutive sampling periods in Hurricane Sam (2021), which underwent substantial intensity changes over three days. During the first and last periods, coinciding with intensification, the ventilation was relatively small due to a lack of spatial correlation between radial flow and θ e azimuthal asymmetries. During the second period, coinciding with weakening, the ventilation was relatively large. The increased ventilation was caused by greater shear associated with an upper-level trough, tilting the vortex, along with dry, low-θ e air wrapping in upshear. The spatial correlation of the radial inflow and anomalously low-θ e air resulted in large ventilation at mid-to-upper levels. Additionally, at low-to-mid levels, there was evidence of mesoscale inflow of low-θ e air in the stationary band complex. The location of these radial ventilation pathways and their effects on Sam’s intensity are consistent with previous idealized and real-case modeling studies. More generally, this method offers a way to monitor ventilation changes in tropical cyclones, particularly when there is full-troposphere sampling around and within a tropical cyclone’s core.

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Pavlos Kollias
,
Greg M. McFarquhar
,
Eric Bruning
,
Paul J. DeMott
,
Matthew R. Kumjian
,
Paul Lawson
,
Zachary Lebo
,
Timothy Logan
,
Kelly Lombardo
,
Mariko Oue
,
Greg Roberts
,
Raymond A. Shaw
,
Susan C. van den Heever
,
Mengistu Wolde
,
Kevin R. Barry
,
David Bodine
,
Roelof Bruintjes
,
Venkatachalam Chandrasekar
,
Andrew Dzambo
,
Thomas C. J. Hill
,
Michael Jensen
,
Francesc Junyent
,
Sonia M. Kreidenweis
,
Katia Lamer
,
Edward Luke
,
Aaron Bansemer
,
Christina McCluskey
,
Leonid Nichman
,
Cuong Nguyen
,
Ryan J. Patnaude
,
Russell J. Perkins
,
Heath Powers
,
Keyvan Ranjbar
,
Eric Roux
,
Jeffrey Snyder
,
Bernat P. Treserras
,
Peisang Tsai
,
Nathan A. Wales
,
Cory Wolff
,
Nithin Allwayin
,
Ben Ascher
,
Jason Barr
,
Yishi Hu
,
Yongjie Huang
,
Miles Litzmann
,
Zackary Mages
,
Katherine McKeown
,
Saurabh Patil
,
Elise Rosky
,
Kristofer Tuftedal
,
Min-Duan Tzeng
, and
Zeen Zhu

Abstract

Convective clouds play an important role in the Earth’s climate system and are a known source of extreme weather. Gaps in our understanding of convective vertical motions, microphysics, and precipitation across a full range of aerosol and meteorological regimes continue to limit our ability to predict the occurrence and intensity of these cloud systems. Towards improving predictability, the National Science Foundation (NSF) sponsored a large field experiment entitled “Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE).” ESCAPE took place between 30 May - 30 Sept. 2022 in the vicinity of Houston, TX because this area frequently experiences isolated deep convection that interacts with the region's mesoscale circulations and its range of aerosol conditions.

ESCAPE focused on collecting observations of isolated deep convection through innovative sampling, and on developing novel analysis techniques. This included the deployment of two research aircraft, the National Research Council of Canada Convair-580 and the Stratton Park Engineering Company Learjet, which combined conducted 24 research flights from 30 May to 17 June. On the ground, three mobile X-band radars, and one mobile Doppler lidar truck equipped with soundings, were deployed from 30 May to 28 June. From 1 August to 30 Sept. 2022, a dual-polarization C-band radar was deployed and operated using a novel, multi-sensor agile adaptive sampling strategy to track the entire lifecycle of isolated convective clouds. Analysis of the ESCAPE observations has already yielded preliminary findings on how aerosols and environmental conditions impact the convective life cycle.

Open access
Larry W. O’Neill
,
Dudley B. Chelton
,
Ernesto Rodríguez
,
Roger Samelson
, and
Alexander Wineteer

Abstract

We propose a method to reconstruct sea surface height anomalies (SSHA) from vector surface currents and winds. This analysis is motivated by the proposed satellite ODYSEA, which is a Doppler scatterometer that measures coincident surface vector winds and currents. If it is feasible to estimate SSHA from these measurements, then ODYSEA could provide collocated fields of SSHA, currents, and winds over a projected wide swath of ∼1700 km. The reconstruction also yields estimates of the low-frequency surface geostrophic, Ekman, irrotational, and nondivergent current components and a framework for separation of balanced and unbalanced motions. The reconstruction is based on a steady-state surface momentum budget including the Ekman drift, Coriolis acceleration, and horizontal advection. The horizontal SSHA gradient is obtained as a residual of these terms, and the unknown SSHA is solved for using a Helmholtz–Hodge decomposition given an imposed SSHA boundary condition. We develop the reconstruction using surface currents, winds, and SSHA off the U.S. West Coast from a 43-day coupled ROMS–WRF simulation. We also consider how simulated ODYSEA measurement and sampling errors and boundary condition uncertainties impact reconstruction accuracy. We find that temporal smoothing of the currents for periods of 150 h is necessary to mitigate large reconstruction errors associated with unbalanced near-inertial motions. For the most realistic case of projected ODYSEA measurement noise and temporal sampling, the reconstructed SSHA fields have an RMS error of 2.1 cm and a model skill (squared correlation) of 0.958 with 150-h resolution. We conclude that an accurate SSHA reconstruction is feasible using information measured by ODYSEA and external SSHA boundary conditions.

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Christopher Wyburn-Powell
and
Alexandra Jahn

Abstract

Summer Arctic sea ice is declining rapidly but with superimposed variability on multiple timescales that introduces large uncertainties into projections of future sea ice loss. To better understand what drives at least part of this variability, we show how a simple linear model can link dominant modes of climate variability to low-frequency regional Arctic sea ice concentration (SIC) anomalies. Focusing on September, we find skillful projections from global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) at lead times of 4-20 years, with up to 60% of observed low-frequency variability explained at a 5-year lead time. The dominant driver of low-frequency SIC variability is the Interdecadal Pacific Oscillation (IPO) which is positively correlated with SIC anomalies in all regions up to a lead time of 15 years, but with large uncertainty between GCMs and internal variability realization. The Niño 3.4 Index and Atlantic Multidecadal Oscillation have better agreement between GCMs of being positively and negatively related, respectively, with low-frequency SIC anomalies for at least 10-year lead times. The large variation between GCMs and between members within large ensembles indicate the diverse simulation of teleconnections between the tropics and Arctic sea ice, and the dependence on initial climate state. Further, the influence of the Niño 3.4 Index was found to be sensitive to the background climate. Our results suggest that, based on the 2022 phases of dominant climate variability modes, enhanced loss of sea ice area across the Arctic is likely during the next decade.

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Manho Park
,
Zhonghua Zheng
,
Nicole Riemer
, and
Christopher W. Tessum

Abstract

We developed and applied a machine-learned discretization for one-dimensional (1-D) horizontal passive scalar advection, which is an operator component common to all chemical transport models (CTMs). Our learned advection scheme resembles a second-order accuracy, three-stencil numerical solver, but differs from a traditional solver in that coefficients for each equation term are output by a neural network rather than being theoretically-derived constants. We subsampled higher-resolution simulation results—resulting in up to 16× larger grid size and 64× larger timestep—and trained our neural network-based scheme to match the subsampled integration data. In this way, we created an operator that is low-resolution (in time or space) but can reproduce the behavior of a high-resolution traditional solver. Our model shows high fidelity in reproducing its training dataset (a single 10-day 1-D simulation) and is similarly accurate in simulations with unseen initial conditions, wind fields, and grid spacing. In many cases, our learned solver is more accurate than a low-resolution version of the reference solver, but the low-resolution reference solver achieves greater computational speedup (500× acceleration) over the high-resolution simulation than the learned solver is able to (18× acceleration). Surprisingly, our learned 1-D scheme—when combined with a splitting technique—can be used to predict 2-D advection, and is in some cases more stable and accurate than the low-resolution reference solver in 2-D. Overall, our results suggest that learned advection operators may offer a higher-accuracy method for accelerating CTM simulations as compared to simply running a traditional integrator at low resolution.

Open access
Wenting Wang
,
Hongrong Shi
,
Disong Fu
,
Mengqi Liu
,
Jiawei Li
,
Yunpeng Shan
,
Tao Hong
,
Dazhi Yang
, and
Xiang’ao Xia

Abstract

Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%.

Significance Statement

Numerical weather prediction (NWP) is the “go-to” approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP.

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Haoyu Yang
,
Shaoqing Zhang
,
Jinzhuo Cai
,
Dong Wang
,
Xiong Deng
, and
Yang Gao

Abstract

Climate model simulations tend to drift away from the real world because of model errors induced by an incomplete understanding and implementation of dynamics and physics. Parameter estimation uses data assimilation methods to optimize model parameters, which minimizes model errors by incorporating observations into the model through state-parameter covariance. However, traditional parameter estimation schemes that simultaneously estimate multiple parameters using observations could fail to reduce model errors because of the low signal-to-noise ratio in the covariance. Here, based on the saturation time scales of model sensitivity that depend on different parameters and model components, we design a new multicycle parameter estimation scheme, where each cycle is determined by the saturation time scale of sensitivity of the model state associated with observations in each climate system component. The new scheme is evaluated using two low-order models. The results show that due to high signal-to-noise ratios sustained during the parameter estimation process, the new scheme consistently reduces model errors as the number of estimated parameters increases. The new scheme may improve comprehensive coupled climate models by optimizing multiple parameters with multisource observations, thereby addressing the multiscale nature of component motions in the Earth system.

Significance Statement

Parameter estimation is used to reduce model errors by optimizing the model parameter values with observational information, which is important for improving long-term predictions. In previous parameter estimation methods, multisource observations have not yet been sufficiently used because the quality and dimension size of the optimized parameters are limited. Here, based on the multiscale nature of component motion in the Earth system, we develop a new parameter estimation method that makes full use of multisource observations. The new method processes the parameters being estimated sequentially according to sensitivity magnitudes and saturation time scales so that the parameters can be continuously optimized. This new method has large application potential for weather and climate reanalyses and predictions.

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