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Abstract: The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented.

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the "Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction"(LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global "hotspot"regions identified here; the ensemble means in some "hotspots"produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Abstract. Sub-seasonal to seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging but has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called Impact of initialized Land Surface temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P), as the first international grass-root effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land/atmosphere interactions. LS4P focuses on process understanding and predictability, hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than forty groups worldwide have participated in this effort, including 21 Earth System Models, 9 regional climate models, and 7 data groups. This paper overviews the history and objectives of LS4P, provides the first phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST in the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation and its S2S prediction. LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: i) inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations which both tend to limit the soil memory; and ii) reanalysis data, that are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.

The characteristics of gravity waves in China are investigated through an extensive analysis of high vertical resolution radiosonde observations collected over eight years across 120 stations, and are subsequently compared to those in the United States. These characteristics encompass energy density, intrinsic frequencies, horizontal and vertical wavelengths, as well as vertical and horizonal propagation directions. China and the United States, situated in mid‐latitude regions with prominent western topographical features, the Qinghai‐Tibet Plateau and Rocky Mountains respectively, demonstrate striking similarities in the generation and distribution of gravity waves. Both landmasses exhibit the strongest gravity waves during winter and the weakest during summer. And within the troposphere, the maximum energy of gravity waves is generated over and immediately downstream of the topographies. In addition, the energy level is amplified in the lower stratosphere. However, unique regional contrasts in summer are result from the differences of summer monsoon influence and the distinct western topographies. The maximum gravity wave energy in summer troposphere is observed over the north side of the Qinghai‐Tibet Plateau in China, contrasting with its location downstream of the Rockies in the United States. Plain Language Summary: Extensive studies have highlighted the global circulation effects of small‐scale atmospheric gravity waves, but our knowledge of the global distribution of gravity waves remains incomplete. High vertical resolution raw radiosonde data achieved at meteorological sounding stations present a potentially invaluable resource of deriving information on gravity waves. Previous studies have used these data achieved in China at a small subset of stations to analyze limited gravity waves parameters, which cannot provide a fully representative sample of the diverse underlying surface and weather conditions across the country and acquire complete parameters of gravity wave. Our study using the complete data set aims to contribute valuable information on the comprehensive characteristics of gravity wave activities in China, and compare these results to those derived from a similar data set in the United States. The results demonstrate many striking similarities in terms of the generation and distribution of gravity waves in two landmasses. However, unique regional contrasts in summer are result from the differences of summer monsoon influence and the distinct height and shape of their respective western topographies. These results will supplement knowledge of the global distribution of gravity waves and be beneficial to the development of the numerical weather prediction models. Key Points: This study derives characteristics of gravity wave in China using high vertical resolution radiosondeThere are many striking similarities in terms of the generation and distribution of gravity waves in China and the United StatesUnique regional contrasts in summer are result from the differences of summer monsoon influence and the distinct western topographies [ABSTRACT FROM AUTHOR]

This study explores the impact of coupling cumulus and planetary boundary layer (PBL) parameterizations on diurnal precipitation forecasting during the plum rainy season in Jiangsu Province, China, using a double grid‐nesting approach. Results show that coherent coupling of cumulus (only in the 15 km grid outer domain [O]) and PBL parameterizations leads to improved forecasting of diurnal variations in the morning, afternoon, and the evening. Increasing the frequency of the Kain‐Fritsch (KF) cumulus scheme in [O] enhances subgrid precipitation while reducing grid‐scale precipitation, resulting in a more accurate representation of daytime convective activities and a reduction in over‐forecasting of evening valley and early‐morning precipitation. Additionally, coupling a suitable PBL scheme mitigates the overpredicted afternoon peak by facilitating turbulent mixing to penetrate higher altitudes with a thicker layer, thereby reducing instability energy accumulation. A higher KF frequency in [O] retains less low tropospheric moisture, reducing moisture convergence into the 1 km grid inner domain [I] and decreasing overpredicted daytime precipitation in [I]. Various PBL schemes produce distinct vertical distributions of turbulent moisture and heat transport, impacting convection and precipitation in [I] resolved by cloud microphysics processes. The coherent coupling of these parameterizations maintains a balanced supply of convective energy and water vapor, significantly improving diurnal precipitation forecasts in [I]. Isolating these parameterizations between nested grids may undermine this improvement. Plain Language Summary: Accurate diurnal precipitation forecasting remains a challenge. This study aims to effectively couple cumulus and planetary boundary layer (PBL) parameterizations to enhance diurnal precipitation forecasts using a double grid‐nesting approach. We showed that coherent coupling of cumulus and PBL parameterizations improves forecasting of diurnal variations during the plum rainy season in Jiangsu Province, China. Increasing the frequency of the KF cumulus scheme more accurately represents daytime convective activities and reduces over‐forecasting of evening valley and early‐morning precipitation. Coupling an appropriate PBL scheme that extends turbulent mixing into deeper layers mitigates the accumulation of instability energy, thereby reducing overpredicting of the afternoon peak. A higher KF frequency in the 15 km grid outer domain [O] retains less low tropospheric moisture, reducing moisture convergence into the 1 km grid inner domain [I] and decreasing overpredicted daytime precipitation in [I]. Diverse PBL schemes result in varying vertical distributions of turbulent moisture and heat transport, affecting explicit convection and precipitation in [I]. Therefore, the cohesive integration of cumulus and PBL parameterizations across nested grids significantly enhances diurnal precipitation forecasts. Key Points: Coherent coupling between cumulus and planetary boundary layer (PBL) parameterizations improves the nested rainfall diurnal cycle forecastIncreasing cumulus parameterization frequency reduces the nested morning and outer‐domain evening valley rainfall overforecastAdjusting PBL parameterization weakens the rainfall afternoon peak and daytime total amount overprediction [ABSTRACT FROM AUTHOR]

The Journal of Geophysical Research: Atmospheres expresses gratitude to the 2854 scientists who reviewed manuscripts for the journal in 2023. Peer review is essential for maintaining the integrity and rigor of scientific research. The reviews have contributed to the improvement of paper quality, the generation of new ideas, and the advancement of young scientists' careers. The journal acknowledges the reviewers' selfless service and dedication to the scientific community and looks forward to their continued support. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

Bioenergy with carbon capture and geological storage (BECCS) is considered one of the top options for both offsetting CO2 emissions and removing atmospheric CO2. BECCS requires using limited land resources efficiently while ensuring minimal adverse impacts on the delicate food‐energy‐water nexus. Perennial C4 biomass crops are productive on marginal land under low‐input conditions avoiding conflict with food and feed crops. The eastern half of the contiguous U.S. contains a large amount of marginal land, which is not economically viable for food production and liable to wind and water erosion under annual cultivation. However, this land is suitable for geological CO2 storage and perennial crop growth. Given the climate variation across the region, three perennials are major contenders for planting. The yield potential and stability of Miscanthus, switchgrass, and energycane across the region were compared to select which would perform best under the recent (2000–2014) and future (2036–2050) climates. Miscanthus performed best in the Midwest, switchgrass in the Northeast and energycane in the Southeast. On average, Miscanthus yield decreased from present 19.1 t/ha to future 16.8 t/ha; switchgrass yield from 3.5 to 2.4 t/ha; and energycane yield increased from 14 to 15 t/ha. Future yield stability decreased in the region with higher predicted drought stress. Combined, these crops could produce 0.6–0.62 billion tonnes biomass per year for the present and future. Using the biomass for power generation with CCS would capture 703–726 million tonnes of atmospheric CO2 per year, which would offset about 11% of current total U.S. emission. Further, this biomass approximates the net primary CO2 productivity of two times the current baseline productivity of existing vegetation, suggesting a huge potential for BECCS. Beyond BECCS, C4 perennial grasses could also increase soil carbon and provide biomass for emerging industries developing replacements for non‐renewable products including plastics and building materials. [ABSTRACT FROM AUTHOR]

Agricultural soils play a dual role in regulating the Earth's climate by releasing or sequestering carbon dioxide (CO2) in soil organic carbon (SOC) and emitting non‐CO2 greenhouse gases (GHGs) such as nitrous oxide (N2O) and methane (CH4). To understand how agricultural soils can play a role in climate solutions requires a comprehensive assessment of net soil GHG balance (i.e., sum of SOC‐sequestered CO2 and non‐CO2 GHG emissions) and the underlying controls. Herein, we used a model‐data integration approach to understand and quantify how natural and anthropogenic factors have affected the magnitude and spatiotemporal variations of the net soil GHG balance in U.S. croplands during 1960–2018. Specifically, we used the dynamic land ecosystem model for regional simulations and used field observations of SOC sequestration rates and N2O and CH4 emissions to calibrate, validate, and corroborate model simulations. Results show that U.S. agricultural soils sequestered 13.2±1.16$$ 13.2\pm 1.16 $$ Tg CO2‐C year−1 in SOC (at a depth of 3.5 m) during 1960–2018 and emitted 0.39±0.02$$ 0.39\pm 0.02 $$ Tg N2O‐N year−1 and 0.21±0.01$$ 0.21\pm 0.01 $$ Tg CH4‐C year−1, respectively. Based on the GWP100 metric (global warming potential on a 100‐year time horizon), the estimated national net GHG emission rate from agricultural soils was 122.3±11.46$$ 122.3\pm 11.46 $$ Tg CO2‐eq year−1, with the largest contribution from N2O emissions. The sequestered SOC offset ~28% of the climate‐warming effects resulting from non‐CO2 GHG emissions, and this offsetting effect increased over time. Increased nitrogen fertilizer use was the dominant factor contributing to the increase in net GHG emissions during 1960–2018, explaining ~47% of total changes. In contrast, reduced cropland area, the adoption of agricultural conservation practices (e.g., reduced tillage), and rising atmospheric CO2 levels attenuated net GHG emissions from U.S. croplands. Improving management practices to mitigate N2O emissions represents the biggest opportunity for achieving net‐zero emissions in U.S. croplands. Our study highlights the importance of concurrently quantifying SOC‐sequestered CO2 and non‐CO2 GHG emissions for developing effective agricultural climate change mitigation measures. [ABSTRACT FROM AUTHOR]

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors. [ABSTRACT FROM AUTHOR]

The editorial board of JGR Atmospheres thanks reviewers who refereed papers in 2022. Plain Language Summary: The editorial board of JGR Atmospheres thanks reviewers who refereed papers in 2022. Key Point: The editors thank the 2022 peer reviewers [ABSTRACT FROM AUTHOR]

Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling. [ABSTRACT FROM AUTHOR]

Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases. [ABSTRACT FROM AUTHOR]

Most climate models still suffer large warm and dry summer biases in the central United States (CUS). As a solution, we improved cumulus parameterization to represent 1) the lifting effect of small-scale rising motions associated with Great Plains low-level jets and midtropospheric perturbations by defining the cloud base at the level of condensation, 2) the constraint of the cumulus entrainment rate depending on the boundary layer depth, and 3) the temperature-dependent cloud-to-rainwater conversion rate. These improvements acted to (i) trigger mesoscale convective systems in unfavorable environmental conditions to enhance total rainfall amount, (ii) lower cloud base and increase cloud depth to increase low-level clouds and reduce surface shortwave radiation, (iii) suppress penetrative cumuli from shallow boundary layers to remedy the overestimation of precipitation frequency, and (iv) increase water detrainment to form sufficient cirrus clouds and balanced outgoing longwave radiation. Much of these effects were nonlocal and nonlinear, where more frequent but weaker convective rainfall led to stronger (and sometimes more frequent) large-scale precipitation remotely. Together, they produced consistently heavier precipitation and colder temperature with a realistic atmospheric energy balance, essentially eliminating the CUS warm and dry biases through robust physical mechanisms. [ABSTRACT FROM AUTHOR]

The Global Ensemble Forecast System version 12 (GEFSv12) has been implemented into National Centers For Environmental Prediction operations since September 2020, which was uncoupled, but increased the horizontal resolution from 34 to 25 km, increased ensemble members from 21 to 31, and extended forecasts from 16 to 35 days. It significantly improved probabilistic forecast skills in many categories, such as precipitation, tropical storms, Madden‐Julian Oscillation (MJO), etc. The improvements resulted from many aspects, including model resolution increase, dynamical core upgrade, advances in hybrid data assimilation and physical parameterizations, and more importantly, from using new stochastic schemes to improve forecast uncertainty. To further improve GEFS's sub‐seasonal forecast skill, a coupled GEFS was built up on the Unified Forecast System prototype version 5 that fully couples an atmospheric model with a land surface model, ocean model, ice model, and wave model. A set of coupled GEFS experiments were conducted to test different horizontal resolutions at approximately 50 and 25 km while adjusting the stochastic parameterization schemes for the atmosphere to better represent forecast uncertainties. The experiments were run for a 2‐year period from October 2017 to September 2019, with one initialization per week at Wednesday 00 UTC, 11 ensemble members, and were forecasted out to 35 days. The coupled GEFS significantly improves 500 hPa height anomaly correlation in week‐1, week‐2, and MJO skills compared to the current operational forecast. The forecast spread of tropical wind is greatly reduced by improved stochastic schemes and matches well with the forecast root mean square errors. The correlation of forecast error variance and ensemble variance is improved for the coupled GEFSs. Meanwhile, the spread of MJO has been greatly reduced for the coupled GEFSs to improve the MJO forecast uncertainty. Plain Language Summary: The Unified Forecast System is a new community‐based, atmosphere‐land‐ocean‐sea ice‐wave‐aerosol coupled, comprehensive Earth modeling system. A Global Ensemble Forecast System (GEFS; version 12; uncoupled) has been implemented into National Centers For Environmental Prediction operations since September 2020 with 31 members, out to 35 days to cover subseasonal prediction. A new fully coupled GEFS in this investigation has demonstrated an overall better performance around the weather, medium‐range, and weeks 3 and 4 time scales. Forecast uncertainties are quantified by adjusting the coefficients and parameters of the stochastic schemes. Key Points: The coupled Global Ensemble Forecast System has advantages over the uncoupled system for weather and subseasonal predictionsThe forecast uncertainty is quantified by different measures and diagnostic toolsModel resolution is less important for extended range than weather forecasts [ABSTRACT FROM AUTHOR]