Browse

Olawale James Ikuyajolu
,
Luke Van Roekel
,
Steven R Brus
,
Erin E Thomas
,
Yi Deng
, and
James J Benedict

Abstract

This study investigates the sensitivity of the Madden-Julian Oscillation (MJO) to changes to the bulk flux parameterization and the role of ocean surface waves in air-sea coupling using a fully-coupled ocean-atmosphere-wave model. The atmospheric and ocean model components of the Energy Exascale Earth System Model (E3SM) are coupled to a spectral wave model, WAVEWATCH III (WW3). Two experiments with wind speed dependent bulk algorithms, NCAR (Large and Yeager 2004, 2009) & COARE3.0a (Fairall et al. 2003), and one experiment with wave-state dependent flux (COR3.0a-WAV) were conducted. We modify COARE3.0a to include surface roughness calculated within WW3 and also account for the buffering effect of waves on the relative difference between air-side and ocean-side momentum flux. Differences in surface fluxes, primarily caused by discrepancies in drag coefficients, result in significant differences in MJO’s properties. While COARE3.0a has better convection-circulation coupling than NCAR, it exhibits anomalous MJO convection east of the dateline. The wave-state dependent flux (COR3.0-WAV) improves the MJO representation over the default COARE3.0 algorithm. Strong easterlies over the Pacific Ocean in COARE3.0a enhance the latent heat flux (LHFLX). This is responsible for the anomalous MJO propagation after the dateline. In COR3.0a-WAV, waves reduce the anomalous easterlies, leading to a decrease in LHFLX and MJO dissipation after the dateline. These findings highlight the role of surface fluxes in MJO simulation fidelity. Most importantly, we show that the proper treatment of wave-induced effects in bulk flux parameterization improves the simulation of coupled climate variability.

Restricted access
Suqiong Hu
,
Wenjun Zhang
,
Masahiro Watanabe
,
Feng Jiang
,
Fei-Fei Jin
, and
Han-Ching Chen

Abstract

El Niño-Southern Oscillation (ENSO), the dominant mode of interannual variability in the tropical Pacific, is well known to affect the extratropical climate via atmospheric teleconnections. Extratropical atmospheric variability may in turn influence the occurrence of ENSO events. The winter North Pacific Oscillation (NPO), as the secondary dominant mode of atmospheric variability over the North Pacific, has been recognized as a potential precursor for ENSO development. This study demonstrates that the pre-existing winter NPO signal is primarily excited by sea surface temperature (SST) anomalies in the equatorial western-central Pacific. During ENSO years with a preceding winter NPO signal, which accounts for approximately 60% of ENSO events observed in 1979–2021, significant SST anomalies emerge in the equatorial western-central Pacific in the preceding autumn and winter. The concurrent presence of local convection anomalies can act as a catalyst for NPO-like atmospheric circulation anomalies. In contrast, during other ENSO years, significant SST anomalies are not observed in the equatorial western-central Pacific during the preceding winter, and correspondingly, the NPO signal is absent. Ensemble simulations using an atmospheric general circulation model driven by observed SST anomalies in the tropical western-central Pacific can well reproduce the interannual variability of observed NPO. Therefore, an alternative explanation for the observed NPO-ENSO relationship is that the preceding winter NPO is a companion to ENSO development, driven by the precursory SST signal in the equatorial western-central Pacific. Our results suggest that the lagged relationship between ENSO and the NPO involves a tropical-extratropical two-way coupling rather than a purely stochastic forcing of the extratropical atmosphere on ENSO.

Restricted access
Peter J. Marinescu
,
Daniel Abdi
,
Kyle Hilburn
,
Isidora Jankov
, and
Liao-Fan Lin

Abstract

Estimates of soil moisture from two National Oceanic and Atmospheric Administration (NOAA) models are compared to in situ observations. The estimates are from a high-resolution atmospheric model with a land surface model [High-Resolution Rapid Refresh (HRRR) model] and a hydrologic model from the NOAA Climate Prediction Center (CPC). Both models produce wetter soils in dry regions and drier soils in wet regions, as compared to the in situ observations. These soil moisture differences occur at most soil depths but are larger at the deeper depths below the surface (100 cm). Comparisons of soil moisture variability are also assessed as a function of soil moisture regime. Both models have lower standard deviations as compared to the in situ observations for all soil moisture regimes. The HRRR model’s soil moisture is better correlated with in situ observations for drier soils as compared to wetter soils—a trend that was not present in the CPC model comparisons. In terms of seasonality, soil moisture comparisons vary depending on the metric, time of year, and soil moisture regime. Therefore, consideration of both the seasonality and soil moisture regime is needed to accurately determine model biases. These NOAA soil moisture estimates are used for a variety of forecasting and societal applications, and understanding their differences provides important context for their applications and can lead to model improvements.

Significance Statement

Soil moisture is an essential variable coupling the land surface to the atmosphere. Accurate estimates of soil moisture are important for forecasting near-surface temperature and moisture, predicting where clouds will form, and assessing drought and fire risks. There are multiple estimates of soil moisture available, and in this study, we compare soil moisture estimates from two different National Oceanic and Atmospheric Administration (NOAA) models to in situ observations. These comparisons include both soil moisture amount and variability and are conducted at several soil depths, in different soil moisture regimes, and for different seasons and years. This comprehensive assessment allows for an accurate assessment of biases within these models that would be missed when conducting analyses more broadly.

Open access
Joshua B. Wadler
,
Joseph J. Cione
,
Samantha Michlowitz
,
Benjamin Jaimes de la Cruz
, and
Lynn K. Shay

Abstract

This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner-core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the pre-storm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJcm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJcm−2 (SST cooling is always less than 2 °C) which demonstrates the importance of initial oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical-dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts.

Restricted access
Shanshan Li
,
Xiaofang Wang
,
Jianhua Sun
,
Zheng Ma
,
Yuanchun Zhang
,
Yuan Gao
,
Yang Hu
, and
Wengang Zhang

Abstract

Convection initiations (CIs) observed using the advanced geosynchronous radiation imager on the Chinese Fengyun-4A satellite were identified over the middle reaches of the Yangtze River basin during warm season (May–September) of 2018–21. A hybrid objective tracking algorithm combining the conventional area overlapping with the Kalman filter method was applied. Subsequently, spatial and temporal variations in the identified CIs and their synoptic circulation patterns were analyzed. The frequency of CIs was highest in August and lowest in May. Nearly 81% of CIs occurred during noon–afternoon (1100–1859 LST), with the highest frequency in the southern mountains of the study region, whereas the CIs with relatively low frequency moved to the plains from afternoon to morning (1700–1059 LST). The diurnal variation of CIs throughout the study region exhibited a unimodal structure, with a peak appearing at noon (1200–1259 LST). CIs during noon–afternoon in July and August had faster cloud-top cooling rates. The synoptic circulations without tropical cyclones during noon–afternoon hours were classified into four patterns by hierarchical clustering; two dominant patterns (i.e., SW-Flows and S-Flows) had broader areas of higher most unstable convective available potential energy (MUCAPE), whereas the 0–3-km shear (SHR3) was the weakest in the S-Flows pattern. It was clear that the high-frequency areas of CIs were most likely to occur in stronger MUCAPE and weaker SHR3 environments, and CIs were more controlled by thermally unstable environments. We further illustrated that CIs tend to concentrate in unstable and moisture flux convergence areas affected by mountains.

Restricted access
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.

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
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.

Restricted access