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Xin-Zhong Liang
,
Drew Gower
,
Jennifer A. Kennedy
,
Melissa Kenney
,
Michael C. Maddox
,
Michael Gerst
,
Guillermo Balboa
,
Talon Becker
,
Ximing Cai
,
Roger Elmore
,
Wei Gao
,
Yufeng He
,
Kang Liang
,
Shane Lotton
,
Leena Malayil
,
Megan L. Matthews
,
Alison M. Meadow
,
Christopher M. U. Neale
,
Greg Newman
,
Amy Rebecca Sapkota
,
Sanghoon Shin
,
Jonathan Straube
,
Chao Sun
,
You Wu
,
Yun Yang
, and
Xuesong Zhang

Abstract

Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.

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-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.

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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.

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Walter Dabberdt
,
Darrel Baumgardner
,
Robert Bornstein
,
Gregory Carmichael
,
Richard Clark
,
Jeffrey Collett
,
Harindra Fernando
,
Efi Foufoula-Georgiou
,
Dev Niyogi
,
Mohan Ramamurthy
,
Alan Robock
, and
Julie Winkler
Open 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
Jun Yin
,
Amilcare Porporato
, and
Lamberto Rondoni

Abstract

While the warming trends of the Earth’s mean temperature are evident at climatological scales, the local temperature at shorter timescales are highly fluctuating. Here we show that the probabilities of such fluctuations are characterized by a special symmetry typical of small systems out of equilibrium. Their nearly universal properties are linked to the fluctuation theorem and reveal that the progressive warming is accompanied by growing asymmetry of temperature distributions. These statistics allow us to project the global temperature variability in the near future, in line with predictions from climate models, providing original insight about future extremes.

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Temple R. Lee
,
Sandip Pal
,
Ronald D. Leeper
,
Tim Wilson
,
Howard J. Diamond
,
Tilden P. Meyers
, and
David D. Turner

Abstract

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings which can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different scenarios, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions (i.e., different near-surface heating rates ( d T d t ), incoming shortwave radiation (SWd ) regimes, and 5-cm soil moisture (SM 05)) to evaluate the High-Resolution Rapid Refresh (HRRR) model, which is a 3-km model used for operational weather forecasting in the U.S. On days with small (large) d T d t , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM 05 biases. On days with small (large) SWd , we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM 05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM 05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identifying model deficiencies which supports improvements to the parameterization schemes used therein. A similar, regime-specific model verification approach may also be used to help evaluate other geophysical models.

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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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