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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-20th 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 timescales. Instead of heat uptake through surface heat fluxes, these asymmetric variations arise primarily from wind-driven heat redistribution. Pacemaker and pre-industrial 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 anti-cyclonic 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.

Restricted access
Hui Guo
,
Ying Hou
,
Yuting Yang
, and
Tim R. McVicar

Abstract

Macroscale hydrological/land surface models are important tools for assessing historical and predicting future characteristics of extreme hydrological events, yet quantitative understandings of how these large-scale models perform in simulating extreme hydrological characteristics remain limited. Here we evaluate simulated high and low flows from 23 macroscale models within three modeling experiments (i.e., 14 climate models from CMIP6, 6 global hydrological models from ISIMIP2a, and 3 land surface models from GLDAS) against observation in 633 unimpaired catchments globally over 1971–2010. Our findings reveal limitations in simulating extreme flow characteristics by these models. Specifically, we find that (i) most models overestimate high-flow magnitudes (bias range: from +15% to +70%) and underestimate low-flow magnitudes (bias range: from −80% to −20%); (ii) interannual variability in high and low flows is reasonably reproduced by ISIMIP2a and GLDAS models but poorly reproduced by CMIP6 models; (iii) no model consistently replicates the observed trend direction in high and low flows in over two-thirds of the catchments, and most models overestimate high-flow trends and underestimate low-flow trends; and (iv) CMIP6 and GLDAS models show timing biases, with early high flows and late low flows, while ISIMIP2a models exhibit the opposite pattern. Furthermore, all models performed better in more humid environments and noncold regions, with model structure and parameterization contributing more to uncertainties than climatic forcings. Overall, our results demonstrate that extreme flow characteristics simulated from current state-of-the-art macroscale models still contain large uncertainties and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs.

Significance Statement

Macroscale hydrological and land surface models represent crucial tools for assessing historical trends and making predictions about future hydrological changes. Nevertheless, our current understanding of the quantitative performance of these large-scale models in simulating extreme hydrological characteristics remains limited. Here, we evaluate simulated high and low flows from 23 state-of-the-art macroscale models against observation in 633 unimpaired catchments globally over 1971–2010. Our results reveal important limitations in the extreme flow characteristics simulated from these models and provide important guidance regarding the robustness of assessing extreme hydrometeorological events based on these modeling outputs. The model evaluation performed herein serves as a pivotal, offering valuable insights to inform the development of the next generation of macroscale hydrological and land surface models.

Restricted access
J. Michael Battalio
and
Juan M. Lora

Abstract

Changes in the vertical and meridional temperature gradients of the atmosphere drive competing influences on storm track activity. We apply local eddy energetics to the ERA5, JRA55, MERRA2, and NCEP2 reanalyses during 1980–2020 to determine the locations, magnitudes, and trends of the energy transfer mechanisms for synoptic-scale eddies. Eddy kinetic energy (EKE) increases more rapidly in the Southern Hemisphere at all altitudes and seasons, with larger increases during austral winter and spring. In the Northern Hemisphere, increases occur within the Atlantic and Pacific storm tracks at pressures below 300 hPa but only during boreal winter and spring and confined within a narrow zonal band; EKE decreases during boreal summer and fall. Most EKE changes correspond with trends in baroclinic energy conversion upstream of storm tracks and appear to align with increases in the growth rate of the most unstable baroclinic mode. Barotropic energy conversion of EKE to the mean flow becomes locally more intense downstream of the storm tracks. Conversion of EKE to long-period eddies plays a minor role averaged over a hemisphere but can be important locally. The primary strengthening pathway for removal of EKE is a combination of surface friction and viscous dissipation. The increased baroclinic conversion in the Southern Hemisphere appears related to upper-level tropical temperature increases. In the Northern Hemisphere, baroclinic conversion is enabled by a combination of increased vertical heat fluxes and a region of temperature increases within 30°–60°N.

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Free access
Minghua Zheng
,
Ryan Torn
,
Luca Delle Monache
,
James Doyle
,
Fred Martin Ralph
,
Vijay Tallapragada
,
Christopher Davis
,
Daniel Steinhoff
,
Xingren Wu
,
Anna Wilson
,
Caroline Papadopoulos
, and
Patrick Mulrooney

Abstract

During a 6-day intensive observing period in January 2021, Atmospheric River Reconnaissance (AR Recon) aircraft sampled a series of atmospheric rivers (ARs) over the northeastern Pacific that caused heavy precipitation over coastal California and the Sierra Nevada. Using these observations, data denial experiments were conducted with a regional modeling and data assimilation system to explore the impacts of research flight frequency and spatial resolution of dropsondes on model analyses and forecasts. Results indicate that dropsondes significantly improve the representation of ARs in the model analyses and positively impact the forecast skill of ARs and quantitative precipitation forecasts (QPF), particularly for lead times > 1 day. Both reduced mission frequency and reduced dropsonde horizontal resolution degrade forecast skill. On the other hand, experiments that assimilated only G-IV data and experiments that assimilated both G-IV and C-130 data show better forecast skill than experiments that only assimilated C-130 data, suggesting that the additional information provided by G-IV data is necessary for improving forecast skill. Although this is a case study, the 6-day period studied encompassed multiple AR events that are representative of typical AR behavior. Therefore, the results indicate that future operational AR Recon missions incorporate daily mission or back-to-back flights, maintain current dropsonde spacing, support high-resolution data transfer capacity on the C-130s, and utilize G-IV aircraft in addition to C-130s.

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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 non-linear 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 Extended Pathfinder Atmospheres (Patmos-x) observational dataset.

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Adrien Pierre
,
Daniel F. Nadeau
,
Antoine Thiboult
,
Alain N. Rousseau
,
François Anctil
,
Charles P. Deblois
,
Maud Demarty
,
Pierre-Erik Isabelle
, and
Alain Tremblay

Abstract

The hydrological processes of cascading hydroelectric reservoirs differ from those of lakes, due to the importance of the inflows and outflows that vary with energy demand. These heat and water advection terms are rarely considered in water body energy balance analyses even though reservoirs are common man-made structures, especially in North America, and thus may affect the regional climate. This study provides a comprehensive assessment of the water and energy balance of the 85-km2 Romaine-2 northern reservoir (50.69°N, 63.24°W), mean depth of 44 m, highlighting the significant contribution of the advection heat fluxes. The water balance input was primarily controlled by upstream (turbine) inflows (77.6%), while lateral (natural) inflows and direct precipitation represented 21.2% and 1.2%, respectively. As for the reservoir’s heat budget, the net advection of heat accounted on average for 25.0% of the input, of which net radiation was the largest component (73.3%). After accounting for the absence of energy balance closure, latent heat and sensible heat fluxes represented 73.2% and 25.1% of total energy output from the reservoir, respectively. The thermal regime was influenced by the hydrological flow conditions, which were regulated by reservoir management. This played a major role in the evolution of the thermocline and the temperature of the epilimnion, and ultimately, in the dynamics of the turbulent heat fluxes. This study suggests that the heat advection term represents a large fraction of the heat budget of northern reservoirs and should be properly considered.

Restricted access
Zhiwei Zhen
,
Huikyo Lee
,
Ignacio Segovia-Dominguez
,
Meichen Huang
,
Yuzhou Chen
,
Michael Garay
,
Daniel Crichton
, and
Yulia R. Gel

Abstract

Virtually all aspects of our societal functioning—from food security to energy supply to healthcare—depend on the dynamics of environmental factors. Nevertheless, the social dimensions of weather and climate are noticeably less explored by the artificial intelligence community. By harnessing the strength of geometric deep learning (GDL), we aim to investigate the pressing societal question the potential disproportional impacts of air quality on COVID-19 clinical severity. To quantify air pollution levels, here we use aerosol optical depth (AOD) records that measure the reduction of the sunlight due to atmospheric haze, dust, and smoke. We also introduce unique and not yet broadly available NASA satellite records (NASAdat) on AOD, temperature, and relative humidity and discuss the utility of these new data for biosurveillance and climate justice applications, with a specific focus on COVID-19 in the states of Texas and Pennsylvania. The results indicate, in general, that the poorer air quality tends to be associated with higher rates for clinical severity and, in the case of Texas, that this phenomenon particularly stands out in Texan counties characterized by higher socioeconomic vulnerability. This, in turn, raises a concern of environmental injustice in these socioeconomically disadvantaged communities. Furthermore, given that one of NASA’s recent long-term commitments is to address such inequitable burden of environmental harm by expanding the use of Earth science data such as NASAdat, this project is one of the first steps toward developing a new platform integrating NASA’s satellite observations with deep learning (DL) tools for social good.

Significance Statement

By leveraging the strengths of modern deep learning models, particularly, graph neural networks to describe complex spatiotemporal dependencies and by introducing new NASA satellite records, this study aims to investigate the problem of potential environmental injustice associated with COVID-19 clinical severity and caused by disproportional impacts of poor air quality on disadvantaged socioeconomic populations.

Open access
Haochen Tan
,
Rao Kotamarthi
, and
Pallav Ray

Abstract

The surface sensible heat flux induced by precipitation (QP ) is a consequence of the temperature difference between the surface and the rain droplets. Despite its seemingly negligible nature, QP is frequently omitted from both meteorological and climatological models. Nevertheless, it is important to acknowledge the numerous occasions in which the instantaneous values of QP can be significant, particularly during extreme precipitation events. This study undertakes a comprehensive assessment of QP across the contiguous United States (CONUS) utilizing high-resolution reanalysis, observational data, and numerical modeling to examine the influence of QP on precipitation and the surface energy budget. The findings indicate that the spatial distribution of QP climatology is analogous to that of precipitation, with magnitudes ranging from 2 to 3 W m−2 predominantly over the Midwest and Southeast regions. A seasonal analysis of QP reveals that the highest values occurring during the June–August (JJA) period, averaging 3.18 W m−2. Peak QP values of approximately 4 W m−2 are observed during JJA over the Great Plains region. We hypothesize that the QP during an extreme precipitation event would be nonnegligible and have a significant impact on the local weather. To test this conjecture, we perform high-resolution simulations with and without QP during an extreme precipitation event over the Chicago Metropolitan Area (CMA). The results show that the QP may be a dominant factor compared to other components of surface heat flux during the zenith of precipitation hours. Also, QP has the potential to not only diminish precipitation but also alter and reconfigure the remaining surface energy budget components.

Open access
Alex J. Cannon

Abstract

Canadian climate service providers offer projections from the Coupled Model Intercomparison Project (CMIP6) to help inform climate change mitigation and adaptation decisions. CMIP6 includes several “hot” climate models whose sensitivity to greenhouse gas forcings exceeds the likely range inferred from multiple lines of evidence. Global warming estimates assessed in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) were reduced by applying observational constraints on the historical rate of warming to the CMIP6 ensemble. This study assesses whether globally constrained CMIP6 projections for Canada are appreciably different from unconstrained projections. Two constraints are considered: one that removes models whose transient climate response lies outside the AR6 assessed range (TCRlikely), and the other that weights models to match the assessed distribution of equilibrium climate sensitivity (ECSall). Both constraints lead to appreciably cooler and drier projections than the unconstrained ensemble, with the strongest reductions seen in the upper end of the ensemble range, high-emissions scenario, end-of-century time period, and northern regions of Canada. In this case, constrained projections of annual mean temperature are 2°–3°C cooler than the unconstrained projections, whereas projections of annual total precipitation are typically 20%–40% drier. Appreciable differences are also detected in the ensemble median of temperature extreme indices. Based on these results, it is recommended that a constrained ensemble be considered for regional projections to avoid the “hot model” problem. Alternatively, projections can be communicated conditional on a specified level of global warming, with global constraints then used to inform the timing of the warming level exceedance.

Open access