Development of a multidecadal land reanalysis over High Mountain Asia

Anthropogenic and climatic changes affect the water and energy cycles in High Mountain Asia (HMA), home to over two billion people and the largest reservoirs of freshwater outside the polar zone. Despite their significant importance for water management, consistent and reliable estimates of water storage and fluxes over the region are lacking because of the high uncertainties associated with the estimates of atmospheric conditions and human management. Here, we relied on multivariate data assimilation (MVDA) to provide estimates of energy and water storage and fluxes that reflect the processes occurring in the region such as greening and irrigation-driven groundwater depletion. We developed and employed an ensemble precipitation estimate by blending different precipitation products thereby reducing the uncertainties and inconsistencies associated with precipitation in HMA. Then, we assimilated five variables that capture the changes in hydrology in response to climate change and anthropogenic activities. Overall, our results have shown that MVDA has allowed a better representation of the land surface processes including greening and irrigation-driven groundwater depletion in HMA.

Uniformly elevated future heat stress in China driven by spatially heterogeneous water vapor changes

The wet bulb temperature (Tw) has gained considerable attention as a crucial indicator of heat-related health risks. Here we report south-to-north spatially heterogeneous trends of Tw in China over 1979-2018. We find that actual water vapor pressure (Ea) changes play a dominant role in determining the different trend of Tw in southern and northern China, which is attributed to the faster warming of high-latitude regions of East Asia as a response to climate change. This warming effect regulates large-scale atmospheric features and leads to extended impacts of the South Asia high (SAH) and the western Pacific subtropical high (WPSH) over southern China and to suppressed moisture transport. Attribution analysis using climate model simulations confirms these findings. We further find that the entire eastern China, that accommodates 94% of the country's population, is likely to experience widespread and uniform elevated thermal stress the end of this century. Our findings highlight the necessity for development of adaptation measures in eastern China to avoid adverse impacts of heat stress, suggesting similar implications for other regions as well.

Coping with the concurrent heatwaves and ozone extremes in China under a warming climate

Intensive human activity has brought about unprecedented climate and environmental crises, in which concurrent heatwaves and ozone extremes pose the most serious threats. However, a limited understanding of the comprehensive mechanism hinders our ability to mitigate such compound events, especially in densely populated regions like China. Here, based on field observations and climate-chemistry coupled modelling, we elucidate the linkage between human activities and the climate system in heat-related ozone pollution. In China, we have observed that both the frequency and intensity of heatwaves have almost tripled since the beginning of this century. Moreover, these heatwaves are becoming more common in urban clusters with serious ozone pollution. Persistent heatwaves during the extremely hot and dry summers of 2013 and 2022 accelerated photochemical ozone production by boosting anthropogenic and biogenic emissions, and aggravated ozone accumulation by suppressing dry deposition due to water-stressed vegetation, leading to a more than 30% increase in ozone pollution in China's urban areas. The sensitivity of ozone to heat is demonstrated to be substantially modulated by anthropogenic emissions, and China's clean air policy may have altered the relationship between ozone and temperature. Climate model projections further highlight that the high-emission climate-socioeconomic scenario tends to intensify the concurrent heat and ozone extremes in the next century. Our results underscore that the implementation of a strict emission strategy will significantly reduce the co-occurrence of heatwaves and ozone extremes, achieving climate and environmental co-benefits.

Development of a composite drought indicator for operational drought monitoring in the MENA region

This paper presents the composite drought indicator (CDI) that Jordanian, Lebanese, Moroccan, and Tunisian government agencies now produce monthly to support operational drought management decision making, and it describes their iterative co-development processes. The CDI is primarily intended to monitor agricultural and ecological drought on a seasonal time scale. It uses remote sensing and modelled data inputs, and it reflects anomalies in precipitation, vegetation, soil moisture, and evapotranspiration. Following quantitative and qualitative validation assessments, engagements with policymakers, and consideration of agencies' technical and institutional capabilities and constraints, we made changes to CDI input data, modelling procedures, and integration to tailor the system for each national context. We summarize validation results, drought modelling challenges and how we overcame them through CDI improvements, and we describe the monthly CDI production process and outputs. Finally, we synthesize procedural and technical aspects of CDI development and reflect on the constraints we faced as well as trade-offs made to optimize the CDI for operational monitoring to support policy decision-making-including aspects of salience, credibility, and legitimacy-within each national context.

Nitrate budget of a terrestrial-to-marine continuum in South China: Insights from isotopes and a Markov chain Monte Carlo model

Anthropogenic nitrate (NO3-) production has been increasing and is exported to the ocean via river networks, causing eutrophication and ecological damage. While studies have focused on river NO3- pollution, what has been lacking is the quantification of the sources of NO3- in coastal rivers. This study applied the dual isotopes (δ15N/δ18O-NO3-) to quantify the sources and their fluxes of NO3- in two inflow rivers of the Qinzhou Bay. By adding our results to the NO3- source apportionment in Qinzhou Bay, we, for the first time, established the NO3- budgets of the terrestrial-to-marine continuum in both high- and low-flow seasons. We quantitatively showed the direct and indirect roles (e.g., the stimulation of nitrification by sewage ammonium-NH4+) of terrestrial sources in driving the high NO3- loading in the estuary. The results highlighted the necessity to consider coastal rivers and estuary as a whole, which could shed light on the effective reduction of NO3- pollution in coastal environments.

Reduced global plant respiration due to the acclimation of leaf dark respiration coupled with photosynthesis

Leaf dark respiration (Rd ) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that Rd and Rubisco carboxylation capacity (Vcmax ) at 25°C (Rd,25 , Vcmax,25 ) are coordinated so that Rd,25 variations support Vcmax,25 at a level allowing full light use, with Vcmax,25 reflecting daytime conditions (for photosynthesis), and Rd,25 /Vcmax,25 reflecting night-time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5-yr warming experiment, and spatially using an extensive field-measurement data set. We compared the results to three published alternatives: Rd,25 declines linearly with daily average prior temperature; Rd at average prior night temperatures tends towards a constant value; and Rd,25 /Vcmax,25 is constant. Our hypothesis accounted for more variation in observed Rd,25 over time (R2  = 0.74) and space (R2  = 0.68) than the alternatives. Night-time temperature dominated the seasonal time-course of Rd , with an apparent response time scale of c. 2 wk. Vcmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global Rd in response to rising CO2 and warming than is projected by the two of three alternative hypotheses, and by current models.

A prospective cohort study linking migration, climate, and malaria risk in the Peruvian Amazon

Migration is an important risk factor for malaria transmission for malaria transmission, creating networks that connect Plasmodium between communities. This study aims to understand the timing of why people in the Peruvian Amazon migrated and how characteristics of these migrants are associated with malaria risk. A cohort of 2,202 participants was followed for three years (July 2006 - October 2009), with thrice-weekly active surveillance to record infection and recent travel, which included travel destination(s) and duration away. Migration occurred more frequently in the dry season, but the 7-day rolling mean (7DRM) streamflow was positively correlated with migration events (OR 1.25 (95% CI: 1.138, 1.368)). High-frequency and low-frequency migrant populations reported 9.7 (IRR 7.59 (95% CI:.381, 13.160)) and 4.1 (IRR 2.89 (95% CI: 1.636, 5.099)) times more P. vivax cases than those considered non-migrants and 30.7 (IRR 32.42 (95% CI: 7.977, 131.765)) and 7.4 (IRR 7.44 (95% CI: 1.783, 31.066)) times more P. falciparum cases, respectively. High-frequency migrants employed in manual labour within their community were at 2.45 (95% CI: 1.113, 5.416) times higher risk than non-employed low-frequency migrants. This study confirms the importance of migration for malaria risk as well as factors increasing risk among the migratory community, including, sex, occupation, and educational status.

Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP-BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Restructuring and serving web-accessible streamflow data from the NOAA National Water Model historic simulations

In 2016, the National Oceanic and Atmospheric Administration deployed the first iteration of an operational National Water Model (NWM) to forecast the water cycle in the continental United States. With many versions, an hourly, multi-decadal historic simulation is made available to the public. In all released to date, the files containing simulated streamflow contain a snapshot of model conditions across the entire domain for a single timestep which makes accessing  time series a technical and resource-intensive challenge. In the most recent release, extracting a complete streamflow time series for a single location requires managing 367,920 files (~16.2 TB). In this work we describe a reproducable process for restructuring a sequential set of NWM steamflow files for efficient time series access and provide restructured datasets for versions 1.2 (1993-2018), 2.0 (1993-2020), and 2.1 (1979-2022). These datasets have been made accessible via an OPeNDAP enabled THREDDS data server for public use and a brief analysis highlights the latest version of the model should not be assumed best for all locations. Laslty, we describe an R package that expedites data retrieval with examples for multiple use-cases.

The impact of urban irrigation on the temperature-carbon feedback in U.S. cities

Urban areas experience numerous environmental challenges, among which the anthropogenic emissions of heat and carbon are two major contributors, the former is responsible for the notorious urban heat effect, the latter longterm climate changes. Moreover, the exchange of heat and carbon dioxide are closely interlinked in the built environment, and can form positive feedback loops that accelerate the degradation of urban environmental quality. Among a handful countermeasures for heat and carbon mitigation, urban irrigation is believed to be effective in cooling, yet the understanding of its impact on the co-evolution of heat and carbon emission remains obscure. In this study, we conducted multiphysics urban climate modeling for all urban areas in the contiguous United States, and evaluated the irrigation-induced cooling and carbon mitigation. Furthermore, we assessed the impact of urban irrigation on the potential heat-carbon feedback loop, with their strength of coupling quantified by an advanced causal inference method using the convergent cross mapping algorithms. It is found that the impact of urban irrigation varies vastly in geographically different cities, with its local and non-local effect unraveling distinct pathways of heat-carbon feedback mechanism.

Improving model capability in simulating spatiotemporal variations and flow contributions of nitrate export in tile-drained catchments

It is essential to identify the dominant flow paths, hot spots and hot periods of hydrological nitrate-nitrogen (NO3-N) losses for developing nitrogen loads reduction strategies in agricultural watersheds. Coupled biogeochemical transformations and hydrological connectivity regulate the spatiotemporal dynamics of water and NO3-N export along surface and subsurface flows. However, modeling performance is usually limited by the oversimplification of natural and human-managed processes and insufficient representation of spatiotemporally varied hydrological and biogeochemical cycles in agricultural watersheds. In this study, we improved a spatially distributed process-based hydro-ecological model (DLEM-catchment) and applied the model to four tile-drained catchments with mixed agricultural management and diverse landscape in Iowa, Midwestern US. The quantitative statistics show that the improved model well reproduced the daily and monthly water discharge, NO3-N concentration and loading measured from 2015 to 2019 in all four catchments. The model estimation shows that subsurface flow (tile flow + lateral flow) dominates the discharge (70-75%) and NO3-N loading (77-82%) over the years. However, the contributions of tile drainage and lateral flow vary remarkably among catchments due to different tile-drained area percentages and the presence of farmed potholes (former depressional wetlands that have been drained for agricultural production). Furthermore, we found that agricultural management (e.g. tillage and fertilizer management) and catchment characteristics (e.g. soil properties, farmed potholes, and tile drainage) play important roles in predicting the spatial distributions of NO3-N leaching and loading. The simulated results reveal that the model improvements in representing water retention capacity (snow processes, soil roughness, and farmed potholes) and tile drainage improved model performance in estimating discharge and NO3-N export at a daily time step, while improvement of agricultural management mainly impacts NO3-N export prediction. This study underlines the necessity of characterizing catchment properties, agricultural management practices, flow-specific NO3-N movement, and spatial heterogeneity of NO3-N fluxes for accurately simulating water quality dynamics and predicting the impacts of agricultural conservation nutrient reduction strategies.

Continental United States climate projections based on thermodynamic modification of historical weather

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980-2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020-2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

Influence of the freezing level on atmospheric rivers in High Mountain Asia: WRF case studies of orographic precipitation extremes

Atmospheric rivers (ARs) reach High Mountain Asia (HMA) about 10 days per month during the winter and spring, resulting in about 20 mm day-1 of precipitation. However, a few events may exceed 100 mm day-1, providing most of the total winter precipitation and increasing the risk of precipitation-triggered landslides and flooding, particularly when the height of the height of the 0 ∘C isotherm, or freezing level is above-average. This study shows that from 1979 to 2015, integrated water vapor transport (IVT) during ARs that reach Western HMA has increased 16% while the freezing level has increased up to 35 m. HMA ARs that have an above-average freezing level result in 10-40% less frozen precipitation compared to ARs with a below-average freezing level. To evaluate the importance of these trends in the characteristics of ARs, we investigate mesoscale processes leading to orographic precipitation using Advanced Weather Research and Forecasting (ARW-WRF) simulations at 6.7 km spatial resolution. We contrast two above- and below- average freezing level AR events with otherwise broadly similar characteristics and show that with a 50-600 m increase in freezing level, the above-average AR resulted in 10-70% less frozen precipitation than the below-average event. This study contributes to a better understanding of climate change-related impacts within HMA's hydrological cycle and the associated hazards to vulnerable communities living in the region.

Volatile organic compounds measured by proton transfer reaction mass spectrometry over the complex terrain of Quintero Bay, Central Chile

This research provides new evidence regarding the different kinds of air quality episodes, and their underlying mechanisms, that frequently impact the urban area of Quintero Bay in Central Chile, which is located along complex coastal terrain and is surrounded by industries. The monitoring campaign was carried out in January 2022 and encompassed two distinctive meteorological regimes. The first part of the month was dominated by a coastal low centered to the south of Quintero, which resulted in prevailing northerly flow (or weak southerlies) and a deep cloud-topped marine boundary layer. After a 2-3-day transition, the latter collapsed, and a clear-sky regime ensued, which was characterized by a shallow boundary layer and strong southerly winds during the daytime that lasted until the end of the campaign. By using proton transfer reaction time of flight mass spectrometry (PTR-TOF-MS) at a high temporal resolution (1 s), we measured high levels of volatile organic compounds (VOCs) during air quality episodes in real time. The episodes detected were associated with different prevailing meteorological regimes, suggesting that different point sources were involved. In the first episode, propene/cyclopropane, butenes, benzene, toluene and ethylbenzene/xylenes were associated with north and northwesterly weak winds. Complaints associated with hydrocarbon odor were reported. The pollution originated from industrial and petrochemical units located to the north of Quintero, which transport and store natural gas, liquified petroleum gas and oil. The second episode was linked to an oil refinery located south of our measurement site. In this case, high levels of phenol, furan and cresols occurred under strong southwesterly winds. During this event, headaches and dizziness were reported. By contrast, the levels of other aromatic compounds (benzene, toluene, ethylbenzene/xylenes) were lower than in the first air pollution episode.

Performance of Different Crop Models in Simulating Soil Temperature

Soil temperature is one of the key factors to be considered in precision agriculture to increase crop production. This study is designed to compare the effectiveness of a land surface model (Noah Multiparameterization (Noah-MP)) against a traditional crop model (Environmental Policy Integrated Climate Model (EPIC)) in estimating soil temperature. A sets of soil temperature estimates, including three different EPIC simulations (i.e., using different parameterizations) and a Noah-MP simulations, is compared to ground-based measurements from across the Central Valley in California, USA, during 2000-2019. The main conclusion is that relying only on one set of model estimates may not be optimal. Furthermore, by combining different model simulations, i.e., by taking the mean of two model simulations to reconstruct a new set of soil temperature estimates, it is possible to improve the performance of the single model in terms of different statistical metrics against the reference ground observations. Containing ratio (CR), Euclidean distance (dist), and correlation co-efficient (R) calculated for the reconstructed mean improved by 52%, 58%, and 10%, respectively, compared to both model estimates. Thus, the reconstructed mean estimates are shown to be more capable of capturing soil temperature variations under different soil characteristics and across different geographical conditions when compared to the parent model simulations.

Performance of Seven Land Surface Schemes in the WRFv4.3 Model for Simulating Precipitation in the Record-Breaking Meiyu Season Over the Yangtze-Huaihe River Valley in China

In 2020, the Yangtze-Huai river valley (YHRV) experienced the highest record-breaking Meiyu season since 1961, which was mainly characterized by the longest duration of precipitation lasting from early-June to mid-July, with frequent heavy rainstorms that caused severe flooding and deaths in China. Many studies have investigated the causes of this Meiyu season and its evolution, but the accuracy of precipitation simulations has received little attention. It is important to provide more accurate precipitation forecasts to help prevent and reduce flood disasters, thereby facilitating the maintenance of a healthy and sustainable earth ecosystem. In this study, we determined the optimal scheme among seven land surface model (LSMs) schemes in the Weather Research and Forecasting model for simulating the precipitation in the Meiyu season during 2020 over the YHRV region. We also investigated the mechanisms in the different LSMs that might affect precipitation simulations in terms of water and energy cycling. The results showed that the simulated amounts of precipitation were higher under all LSMs than the observations. The main differences occurred in rainstorm areas (>12 mm/day), and the differences in low rainfall areas were not significant (<8 mm/day). Among all of the LSMs, the Simplified Simple Biosphere (SSiB) model obtained the best performance, with the lowest root mean square error and the highest correlation. The SSiB model even outperformed the Bayesian model averaging result. Finally, some factors responsible for the differences modeling results were investigated to understand the related physical mechanism.

Interconnected hydrologic extreme drivers and impacts depicted by remote sensing data assimilation

Hydrologic extremes often involve a complex interplay of several processes. For example, flood events can have a cascade of impacts, such as saturated soils and suppressed vegetation growth. Accurate representation of such interconnected processes while accounting for associated triggering factors and subsequent impacts of flood events is difficult to achieve with conceptual hydrological models alone. In this study, we use the 2019 flood in the Northern Mississippi and Missouri Basins, which caused a series of hydrologic disturbances, as an example of such a flood event. This event began with above-average precipitation combined with anomalously high snowmelt in spring 2019. This series of anomalies resulted in above normal soil moisture that prevented crops from being planted over much of the corn belt region. In the present study, we demonstrate that incorporating remote sensing information within a hydrologic modeling system adds substantial value in representing the processes that lead to the 2019 flood event and the resulting agricultural disturbances. This remote sensing data infusion improves the accuracy of soil moisture and snowmelt estimates by up to 16% and 24%, respectively, and it also improves the representation of vegetation anomalies relative to the reference crop fraction anomalies.

The extreme heat wave of July-August 2021 in the Athens urban area (Greece): Atmospheric and human-biometeorological analysis exploiting ultra-high resolution numerical modeling and the local climate zone framework

Greece was affected by a prolonged and extreme heat wave (HW) event (July 28-August 05) during the abnormally hot summer of 2021, with the maximum temperature in Athens, the capital of the country, reaching up to 43.9 °C in the city center. This observation corresponds to the second highest maximum temperature recorded since 1900, based on the historical temperature time series of the National Observatory of Athens weather station at Thissio. In the present study, a multi-scale numerical modeling system is used to analyze the urban climate and thermal bioclimate in the Athens urban area (AUA) in the course of the HW event, as well as during 3 days prior to the heat wave and 3 days after the episode. The system consists of the Weather Research and Forecasting model, the advanced urban scheme BEP/BEM (Building Energy Parameterization/Building Energy Model) and the human-biometeorological model RayMan Pro, and incorporates the local climate zone (LCZ) classification scheme. The system's validation results demonstrated a robust modeling set-up, characterized by high capability in capturing the observed magnitude and diurnal variation of the urban meteorological and heat stress conditions. The analysis of two- and three-dimensional fields of near-surface air temperature, humidity and wind unraveled the interplay of geographical factors (surface relief and proximity to the sea), background atmospheric circulations (Etesians and sea breeze) and HW-related synoptic forcing with the AUA's urban form. These interactions had a significant impact on the LCZs heat stress responsiveness, expressed using the modified physiologically equivalent temperature (mPET), between different regions of the study area, as well as at inter- and intra-LCZ level (statistically significant differences at 95 % confidence interval), providing thus, urban design and health-related implications that can be exploited in human thermal discomfort mitigation strategies in AUA.

Role of urban boundary layer dynamics and ventilation efficiency in a severe air pollution episode in Tehran, Iran

Tehran faces a significant health challenge due to severe air pollution episodes during wintertime, which are associated with high concentrations of fine particulate matter with a diameter less than 2.5 µm (PM2.5). In this study, we investigated meteorology conditions of one of the severe air pollution episodes, occurred from 27th December 2020 to 15th January 2021, using the Weather Research and Forecasting (WRF) model. To gain insights into this episode, we also modeled a clean episode for comparison. Model validation of land surface temperature using remote sensing showed acceptable performance as well as ground observations for other parameters. We then calculated the ventilation coefficient (VC) from the WRF outputs and analyzed the results statistically. Results indicate the severe reduction in both VC and planetary boundary layer height (PBLH) during the polluted episode. We further linked the decrease in PBLH and VC of the polluted episode to a high-pressure system above 1020 hPa. In contrast, the results for the clean episode indicate that the low-pressure system as low as 1010 hPa led to higher PBLH and VC than during the polluted episode. This low-pressure system favors the reduction of PM2.5 levels to lower than 21 μgm−3.

Assessment of meteorological settings on air quality modeling system-a proposal for UN-SDG and regulatory studies in non-homogeneous regions in Brazil

Air quality models are essential tools to meet the United Nations Sustainable Development Goals (UN-SDG) because they are effective in guiding public policies for the management of air pollutant emissions and their impacts on the environment and human health. Despite its importance, Brazil still lacks a guide for choosing and setting air quality models for regulatory purposes. Based on this, the current research aims to assess the combined WRF/CALMET/CALPUFF models for representing SO₂ dispersion over non-homogeneous regions as a regulatory model for policies in Brazilian Metropolitan Regions to satisfy the UN-SDG. The combined system was applied to the Rio de Janeiro Metropolitan Area (RJMA), which is known for its physiographic complexity. In the first step, the WRF model was evaluated against surface-observed data. The local circulation was underestimated, while the prevailing observational winds were well represented. In the second step, it was verified that all CALMET three meteorological configurations performed better for the most frequent wind speed classes so that the largest SO₂ concentrations errors occurred during light winds. Among the meteorological settings in WRF/CALMET/CALPUFF, the joined use of observed and modeled meteorological data yielded the best results for the dispersion of pollutants. This result emphasizes the relevance of meteorological data composition in complex regions with unsatisfactory monitoring given the inherent limitations of prognostic models and the excessive extrapolation of observed data that can generate distortions of reality. This research concludes with the proposal of the WRF/CALMET/CALPUFF air quality regulatory system as a supporting tool for policies in the Brazilian Metropolitan Regions in the framework of the UN-SDG, particularly in non-homogeneous regions where steady-state Gaussian models are not applicable.

Assimilation of Remotely Sensed Leaf Area Index Enhances the Estimation of Anthropogenic Irrigation Water Use

Representation of irrigation in Earth System Models has advanced over the past decade, yet large uncertainties persist in the effective simulation of irrigation practices, particularly over locations where the on-ground practices and climate impacts are less reliably known. Here we investigate the utility of assimilating remotely sensed vegetation data for improving irrigation water use and associated fluxes within a land surface model. We show that assimilating optical sensor-based leaf area index estimates significantly improves the simulation of irrigation water use when compared to the USGS ground reports. For heavily irrigated areas, assimilation improves the evaporative fluxes and gross primary production (GPP) simulations, with the median correlation increasing by 0.1-1.1 and 0.3-0.6, respectively, as compared to the reference datasets. Further, bias improvements in the range of 14-35 mm mo^-1 and 10-82 g m^-2 mo^-1 are obtained in evaporative fluxes and GPP as a result of incorporating vegetation constraints, respectively. These results demonstrate that the use of remotely sensed vegetation data is an effective, observation-informed, globally applicable approach for simulating irrigation and characterizing its impacts on water and carbon states.

Assimilation of blended in situ-satellite snow water equivalent into the National Water Model for improving hydrologic simulation in two US river basins

This study investigates the potential of assimilating a 1/8° blended in situ-satellite snow water equivalent (SWE) product for improving snow and streamflow predictions of the National Water Model (NWM). The blended product is assimilated into the NWM via a three-dimensional variational (3DVAR) scheme and a direct insertion (DI) scheme, with a daily (1d) and a every 5 days (5d) assimilation frequencies. The experiments are for the Upper Colorado River Basin (UCRB) and Susquehanna River Basin (SRB), which feature seasonal and ephemeral snow covers, respectively. Results indicate that 3DVAR with a 5d assimilation frequency generally outperforms the other scenarios. The assimilation of the blended SWE product mitigates the underestimation of SWE evident in the open-loop simulations for both basins and its impacts are more pronounced for UCRB than for SRB since snowfall is the main source of precipitation in the former. Assimilation leads to improved streamflow over a majority of SRB subbasins, but over a minority of UCRB subbasins. The degradations in streamflow for UCRB subbasins are mainly caused by the overestimated SWE. In addition, the open-loop simulation often produces an earlier streamflow peak in UCRB, and this error is mitigated to a limited extent by assimilation. These findings in aggregate suggest that the efficacy of snow assimilation is strongly dependent upon the types of snowpack and differential assimilation methods and frequencies.

Climate change is increasing the risk of a California megaflood

Despite the recent prevalence of severe drought, California faces a broadly underappreciated risk of severe floods. Here, we investigate the physical characteristics of "plausible worst case scenario" extreme storm sequences capable of giving rise to "megaflood" conditions using a combination of climate model data and high-resolution weather modeling. Using the data from the Community Earth System Model Large Ensemble, we find that climate change has already doubled the likelihood of an event capable of producing catastrophic flooding, but larger future increases are likely due to continued warming. We further find that runoff in the future extreme storm scenario is 200 to 400% greater than historical values in the Sierra Nevada because of increased precipitation rates and decreased snow fraction. These findings have direct implications for flood and emergency management, as well as broader implications for hazard mitigation and climate adaptation activities.

Modeling Global Carbon Costs of Plant Nitrogen and Phosphorus Acquisition

Most Earth system models (ESMs) do not explicitly represent the carbon (C) costs of plant nutrient acquisition, which leads to uncertainty in predictions of the current and future constraints to the land C sink. We integrate a plant productivity-optimizing nitrogen (N) and phosphorus (P) acquisition model (fixation & uptake of nutrients, FUN) into the energy exascale Earth system (E3SM) land model (ELM). Global plant N and P uptake are dynamically simulated by ELM-FUN based on the C costs of nutrient acquisition from mycorrhizae, direct root uptake, retranslocation from senescing leaves, and biological N fixation. We benchmarked ELM-FUN with three classes of products: ILAMB, a remotely sensed nutrient limitation product, and CMIP6 models; we found significant improvements in C cycle variables, although the lack of more observed nutrient data prevents a comprehensive level of benchmarking. Overall, we found N and P co-limitation for 80% of land area, with the remaining 20% being either predominantly N or P limited. Globally, the new model predicts that plants invested 4.1 Pg C yr-1 to acquire 841.8 Tg N yr-1 and 48.1 Tg P yr-1 (1994-2005), leading to significant downregulation of global net primary production (NPP). Global NPP is reduced by 20% with C costs of N and 50% with C costs of NP. Modeled and observed nutrient limitation agreement increases when N and P are considered together (r 2 from 0.73 to 0.83).

Impact of COVID-19 lockdown on the atmospheric boundary layer and instability process over Indian region

The abrupt reduction in the human activities during the first lockdown of the COVID-19 pandemic created unprecedented changes in the background atmospheric conditions. Several studies reported the anthropogenic and air quality changes observed during the lockdown. However, no attempts are made to investigate the lockdown effects on the Atmospheric Boundary Layer (ABL) and background instability processes. In this study, we assess the lockdown impacts on the ABL altitude and instability parameters (Convective Available Potential Energy (CAPE) and Convective Inhibition Energy (CINE)) using WRF model simulations. Results showed a unique footprint of COVID-19 lockdown in all these parameters. Increase in the visibility, surface temperature and wind speed and decrease in relative humidity during the lockdown is noticed. However, these responses are not uniform throughout India and are significant in the inland compared to the coastal regions. The spatial variation of temperature (wind speed) and relative humidity shows an increase and decrease over the Indo Gangetic Plain (IGP) and central parts of India by 20% (100%) and 40%, respectively. Increase (80%) in the ABL altitude is larger over the IGP and central parts of India during lockdown of 2020 compared to similar time period in 2015-2019. This increase is attributed to the stronger insolation due to absence of anthropogenic activity and other background conditions. At the same time, CAPE decreased by 98% in the IGP and central parts of India, where it shows an increase in other parts of India. A prominent strengthening of CINE in the IGP and a weakening elsewhere is also noticed. These changes in CAPE and CINE are mainly attributed to the dearth of saturation in lower troposphere levels, which prevented the development of strong adiabatic ascent during the lockdown. These results provide a comprehensive observation and model-based insight for lockdown induced changes in the meteorological and thermo-dynamical parameters.

How does precipitation data influence the land surface data assimilation for drought monitoring?

Droughts are among the costliest natural hazards that occur annually worldwide. Their socioeconomic impacts are significant and widespread, affecting the sustainable development of human societies. This study investigates the influence of different forcing precipitation data in driving Land Surface Models (LSMs) and characterizing drought conditions. Here, we utilize our recently developed LSM data assimilation system for probabilistically monitoring drought over the Contiguous United States (CONUS). The Noah-MP LSM model is forced with two widely used precipitation data including IMERG (Integrated Multi-satellitE Retrievals for GPM) and NLDAS (North American Land Data Assimilation System). Soil moisture and evapotranspiration are known to have a strong relationship in the land-atmospheric interaction processes. Unlike other studies that attempted the individual assimilation of these variables, here we propose a multivariate data assimilation framework. Therefore, in both modeling scenarios, the data assimilation approach is used to integrate remotely sensed MODIS (Moderate Resolution Imaging Spectroradiometer) evapotranspiration and SMAP (Soil Moisture Active Passive) soil moisture observations into the Noah-MP LSM. The results of this study indicate that the source of precipitation data has a significant impact on the performance of LSM data assimilation system for drought monitoring. The findings revealed that NLDAS and IMERG precipitation can result in a significant difference in identifying drought severity depending on the region and time of the year. Furthermore, our analysis indicates that regardless of the precipitation forcing data product used in the land surface data assimilation system, our modeling framework can effectively detect the drought impacts on crop yield. Additionally, we calculated the drought probability based on the ensemble of soil moisture percentiles and found that there exist temporal and spatial discrepancies in drought probability maps generated from the NLDAS and IMERG precipitation forcings.

Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in the context of climate change. The impact of snowpack changes on land surface phenology and its driving factors were investigated in the Tianshan Mountains using the land surface phenology metrics derived from satellited products and a snow dataset from downscaled regional climate model simulations covering the period from 1983 to 2015. The results demonstrated that the annual mean start of growing season (SOS) and length of growing season (LOS) experienced a significant (p < 0.05) decrease and increase with a rate of −2.45 days/decade and 2.98 days/decade, respectively. The significantly advanced SOS and increased LOS were mainly seen in the Western Tianshan Mountains and Ili Valley regions with elevations from 2500 to 3500 m a.s.l and below 3000 m a.s.l, respectively. During the early spring, the significant decline in snow cover fraction (SCF) could advance the SOS. In contrast, snowmelt amount and annual maximum snow water equivalent (SWE) have an almost equally substantial positive correlation with annual maximum vegetation greenness. In particular, the SOS of grassland was the most sensitive to variations of snow cover fraction during early spring than that of other vegetation types, and their strong relationship was mainly located at elevations from 1500 to 2500 m a.s.l. Its greenness was significantly controlled by the annual maximum snow water equivalent in all elevation bands. Both decreased SCF and increased temperature in the early spring caused a significant advance of the SOS, consequently prolonging the LOS. Meanwhile, more SWE and snowmelt amount could significantly promote vegetation greenness by regulating the soil moisture. The results can improve the understanding of the snow ecosystem services in the alpine regions under climate change.

Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach

Ecohydrological models vary in their sensitivity to forcing data and use available information to different extents. We focus on the impact of forcing precision on ecohydrological model behavior particularly by quantizing, or binning, time-series forcing variables. We use rate-distortion theory to quantize time-series forcing variables to different precisions. We evaluate the effect of different combinations of quantized shortwave radiation, air temperature, vapor pressure deficit, and wind speed on simulated heat and carbon fluxes for a multi-layer canopy model, which is forced and validated with eddy covariance flux tower observation data. We find that the model is more sensitive to radiation than meteorological forcing input, but model responses also vary with seasonal conditions and different combinations of quantized inputs. While any level of quantization impacts carbon flux similarly, specific levels of quantization influence heat fluxes to different degrees. This study introduces a method to optimally simplify forcing time series, often without significantly decreasing model performance, and could be applied within a sensitivity analysis framework to better understand how models use available information.

Central tropical Pacific convection drives extreme high temperatures and surface melt on the Larsen C Ice Shelf, Antarctic Peninsula

Northern sections of the Larsen Ice Shelf, eastern Antarctic Peninsula (AP) have experienced dramatic break-up and collapse since the early 1990s due to strong summertime surface melt, linked to strengthened circumpolar westerly winds. Here we show that extreme summertime surface melt and record-high temperature events over the eastern AP and Larsen C Ice Shelf are triggered by deep convection in the central tropical Pacific (CPAC), which produces an elongated cyclonic anomaly across the South Pacific coupled with a strong high pressure anomaly over Drake Passage. Together these atmospheric circulation anomalies transport very warm and moist air to the southwest AP, often in the form of "atmospheric rivers", producing strong foehn warming and surface melt on the eastern AP and Larsen C Ice Shelf. Therefore, variability in CPAC convection, in addition to the circumpolar westerlies, is a key driver of AP surface mass balance and the occurrence of extreme high temperatures.

The Response of Vegetation to Regional Climate Change on the Tibetan Plateau Based on Remote Sensing Products and the Dynamic Global Vegetation Model

Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation.

Evaluation of Snow and Streamflows Using Noah-MP and WRF-Hydro Models in Aroostook River Basin, Maine

Snow influences land–atmosphere interactions in snow-dominated areas, and snow melt contributes to basin streamflows. However, estimating snowpack properties such as the snow depth (SD) and snow water equivalent (SWE) from land surface model simulations remains a challenge. This is, in part, due to uncertainties in the atmospheric forcing variables, which propagate into hydrological model predictions. This study implements the Weather Research and Forecasting (WRF)-Hydro framework with the Noah-Multiparameterization (Noah-MP) land surface model in the NOAA’s National Water Model (NWM) version 2.0 configuration to estimate snow in a single column and subsequently the streamflow across the Aroostook River’s sub-basins in Maine for water years (WY) 2014–2016. This study evaluates how differences between two atmospheric forcing datasets, the North American Land Data Assimilation version 2 (NLDAS-2) and in situ (Station), translate into differences in the simulation of snow. NLDAS-2 was used as the meteorological forcing in the retrospective NWM 2.0 simulations. The results from the single-column study showed that differences in the simulated SWE and SD were linked to differences in the 2 m air temperature (T2m), which influenced the precipitation partitioning of rain and snow, as parameterized in Noah-MP. The negative mean bias of −0.7 K (during the accumulation period) in T2m for NLDAS-2, compared to the Station forcing, was a major factor that contributed to the positive mean bias of +52 mm on average in the peak SWE in the NLDAS-2-forced Noah-MP simulation during the study period. The higher T2m values at the Station led to higher sensible heat fluxes towards the snowpack, which led to a higher amount of net energy at the snow’s surface and melt events during the accumulation season in Station-forced Noah-MP simulations. The results from the retrospective NWM version 2.0′s simulation in the basin showed that the streamflow estimates were closer to the United States Geological Survey gage observations at the two larger sub-basins (NSE = 0.9), which were mostly forested, compared to the two smaller sub-basins (NSE ≥ 0.4), which had more agricultural land-use. This study also showed that the spring snowmelt timing was captured quite well by the timing of the decline in the simulated SWE and SD, providing an early indication of melt in most sub-basins. The simulated fractional snow cover area (fSCA) however provided less information about the changes in snow or onset of snowmelt as it was mostly binary (full snow cover in winter), which differed from the more realistic fSCA values shown by the Moderate Resolution Imaging Spectroradiometer.

Warming/cooling effect of cropland expansion during the 1900s ~ 2010s in the Heilongjiang Province, Northeast of China

Land cover change (LCC) significantly changed the local/regional temperature. This paper attempts to reveal the effects of cropland expansion in different ways on temperature change from the 1900s to 2010s in Heilongjiang Province. To reach this goal, we conducted four simulation research schemes with the coupled Weather Research and Forecast (WRF)-Noah model to investigate the warming/cooling effect of cropland expansion. The results show that cropland expansion exerted different effects with different land-use type conversions. In the last century, the areas with grassland-to-cropland and wetland-to-cropland transition show the warming effect, and the average surface temperature in Heilongjiang Province increased by 0.023 ℃ and 0.024 ℃, respectively. The areas with forest-to-cropland transition show the cooling effect, in which the average temperature decreased by 0.103 ℃. The variation of air temperature is mainly caused by the variation of surface reflectance and surface net radiation flux. The results provide evidence that cropland expansion changes to biophysical landscape characteristics, warming/cooling the land surface and thus enhancing/reducing the temperature, and lead to regional climate change eventually.

The Impact of High-Resolution SRTM Topography and Corine Land Cover on Lightning Calculations in WRF

The goal of this study is to investigate the impact of high-resolution SRTM and Corine Land Cover on the number of cloud–ground lightning flashes and their spatial distribution simulated by a numerical weather-prediction model. Two lightning episodes were selected: (1) over a non-complex terrain and (2) over a complex terrain, the Alps. Significant discrepancies were found in the geographical distribution of the land-cover classes and also in the topography between Corine Land Cover and 30-arc seconds USGS. In general, the timing and the spatial distribution of Cloud-to-Ground (CG) lightning by the model were well-represented when compared to the observations. In general, more CG flashes were calculated by the simulation with USGS Land Cover and topography than the simulation with Corine Land Cover and SRTM topography. It appears that the differences in sensible and latent heat fluxes between the simulations were caused by the differences in land-cover classes. Moreover, differences in the vertical wind speeds, specific humidity, temperature and the convective available potential energy were found when compared to observations, resulting in the differences in cloud–ground lightning flashes between the simulation with the SRTM topography and Corine Land Cover and the simulation with the USGS Land Cover and topography. Using the high-resolution land cover and topography data may help to reduce uncertainties in CG lightning calculations by the model.

UAV-LiDAR Measurement of Vegetation Canopy Structure Parameters and Their Impact on Land–Air Exchange Simulation Based on Noah-MP Model

Land surface processes play a vital role in the exchange of momentum, energy, and mass between the land and the atmosphere. However, the current model simplifies the canopy structure using approximately three to six parameters, which makes the representation of canopy radiation and energy distribution uncertain to a large extent. To improve the simulation performance, more specific canopy structure parameters were retrieved by a UAV-LiDAR observation system and updated into the multiparameterization version of the Noah land surface model (Noah-MP) for a typical forest area. Compared with visible-light photogrammetry, LiDAR retrieved a more accurate vertical canopy structure, which had a significant impact on land–air exchange simulations. The LiDAR solution resulted in a 35.0∼48.0% reduction in the range of perturbations for temperature and another 27.8% reduction in the range of perturbations for moisture. This was due to the canopy structure affecting the radiation and heat fluxes of the forest, reducing their perturbation range by 7.5% to 30.1%. To reduce the bias of the land surface interaction simulation, it will be necessary to improve the method of retrieving the canopy morphological parameterization through UAV-LiDAR on a continued basis in the future.

Modeling Potential Impacts on Regional Climate Due to Land Surface Changes across Mongolia Plateau

Although desertification has greatly increased across the Mongolian Plateau during the last decades of the 20th century, recent satellite records documented increasing vegetation growth since the 21st century in some areas of the Mongolian Plateau. Compared to the study of desertification, the opposite characteristics of land use and vegetation cover changes and their different effects on regional land–atmosphere interaction factors still lack enough attention across this vulnerable region. Using long-term time-series multi-source satellite records and regional climate model, this study investigated the climate feedback to the observed land surface changes from the 1990s to the 2010s in the Mongolia Plateau. Model simulation suggests that vegetation greening induced a local cooling effect, while the warming effect is mainly located in the vegetation degradation area. For the typical vegetation greening area in the southeast of Inner Mongolia, latent heat flux increased over 2 W/m2 along with the decrease of sensible heat flux over 2 W/m2, resulting in a total evapotranspiration increase by 0.1~0.2 mm/d and soil moisture decreased by 0.01~0.03 mm/d. For the typical vegetation degradation area in the east of Mongolia and mid-east of Inner Mongolia, the latent heat flux decreased over 2 W/m2 along with the increase of sensible heat flux over 2 W/m2 obviously, while changes in moisture cycling were spatially more associated with variations of precipitation. It means that precipitation still plays an important role in soil moisture for most areas, and some areas would be at potential risk of drought with the asynchronous increase of evapotranspiration and precipitation.

Comparison of the Forecast Performance of WRF Using Noah and Noah-MP Land Surface Schemes in Central Asia Arid Region

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m−2) and surface latent heat flux in most areas of Xinjiang (<10 W∙m−2) in January, and mainly generated less sensible heat flux in most areas of north Xinjiang and the mountainous regions of southern Xinjiang (≤20 W·m−2) which on the contrary, generated more surface latent heat flux in most parts of Xinjiang (15~20 W·m−2) in July. Meanwhile, the surface soil heat flux generated from Noah-MP was closer to the observations at Hongliuhe and Kelameili stations in January, the ME increased by 17.5% and reduced by 80.7%, respectively, the RMSE decreased by 44.4% and 61.7%, respectively, and closer to the observations at Xiaotang station in July, the ME and RMSE reduced by 19.1% and 20.5%, respectively. Compared to Noah, Noah-MP improved the overall simulation of soil temperature and soil moisture over the northern and eastern Xinjiang (at 10 cm thickness), the ME and RMSE of simulated soil temperature reduced by 85.0% and 13.4% in January, decreased by 78.6% and increased by 6.2% in July, respectively, and the ME and RMSE of simulated soil moisture reduced by 67.2% and 14.9% in January, reduced by 33.3% and 2.8% in July, respectively. Compared to Noah, Noah-MP’s results were lowered for the simulated 10-m wind speed and 2-m air temperature, especially the simulated 2-m air temperature over the cold climate regions of northern Xinjiang, was improved significantly, the ME and RMSE of simulated 10-m wind speed reduced by 0.8% and 4.9% in January, decreased by 6.7% and 2.8% in July, respectively, the ME and RMSE of simulated 2-m air temperature reduced by 2.8% and 1.0% in July, respectively. This study demonstrated the advantage of coupled Noah-MP over the Central Asia arid region, providing the basis for WRF/Noah-MP in future operational applications in the Central Asia arid region.

Investigation of the influence of mineral dust on airborne particulate matter during the COVID-19 epidemic in spring 2020 over China

A regional air quality model system (RAQMS) driven by the Weather Research and Forecasting model (WRF) is applied to investigate the distribution and evolution of mineral dust and anthropogenic aerosols over China in April 2020, when air quality was improved due to reduced human activity during the COVID-19 epidemic, whereas dust storms began to attack China and deteriorated air quality. A dust deflation model was developed and improved mineral dust prediction. Model validation demonstrated that RAQMS was able to reproduce PM₁₀, PM_2.5 and aerosol components reasonably well. China suffered from three dust events in April 2020, with the maximum hourly PM₁₀ concentrations exceeding 700 μg m^-3 in downwind cities over the North China Plain (NCP). Mineral dust dominated PM₁₀ mass (>80%) over the Gobi deserts in north and west China, while it comprised approximately 30-50% of PM₁₀ over wide areas of east China. The domain and monthly mean dust mass fractions in PM₁₀ were estimated to be 47% and 43% over the North China Plain and east China, respectively. On average, mineral dust contributed up to 22% and 21% of PM_2.5 mass over the North China Plain and east China in April 2020, respectively. Sulfate and nitrate produced by heterogeneous chemical reactions on dust surface accounted for approximately 9% and 13% of secondary inorganic aerosols (SIA) concentration over the North China Plain and east China, respectively. The results from this study demonstrated that mineral dust made an important contribution to particulate matter mass during the COVID-19 epidemic in spring 2020 over China.

Identifying Spatial Patterns of Hydrologic Drought over the Southeast US Using Retrospective National Water Model Simulations

Given the sensitivity of natural environments to freshwater availability in the Southeast US, as well as the reliance of many municipal and commercial water consumers on surface water supplies, specific issues related to low river streamflow are apparent. As a result, the need for quantifying the spatial distribution, frequency, and intensity of low flow events (a.k.a., hydrologic drought) is critical to define areas most susceptible to water shortages and subsequent environmental and societal risk. To that end, daily mean discharge values from the National Water Model (NWM) retrospective data (v. 2.0) are used to assess low flow frequency, intensity, and spatial distribution within the Southeast US. Low flow events are defined using the US EPA 7Q10 approach, based on the flow duration curve (FDC) developed using a 1993–2018 period of record. Results reflect the general climatological patterns of the region, with a higher probability of low flow events occurring during the warm season (June–August) while low flow events in the cool season (January–March) are generally less common and have a higher average discharge. Spatial analysis shows substantial regional variability, with an area from southeastern Mississippi through central South Carolina showing higher low flow event frequency during the cool season. This same area is also highlighted in the warm season, albeit along a more expansive area from central Alabama into the piedmont region of North Carolina. Results indicate that the NWM retrospective data are able to show general patterns of hydrologic drought across the Southeast US, although local-scale assessment is limited due to potential issues associated with infiltration and runoff during periods of warm-season convective rainfall.

Guidelines and a supporting toolbox for parameterising key soil hydraulic properties in hydrological studies and broader integrated modelling

Information on soil hydraulic properties (e.g. soil moisture pressure relationships and hydraulic conductivity) is valuable for a wide range of disciplines including hydrology, ecology, environmental management and agriculture. However, this information is often not readily available as direct measurements are costly and time-consuming. Furthermore, as more complex representations of soils are being built into environmental models, users and developers often require sound hydraulic property information, while having limited access to specialist knowledge. Although indirect methods have been developed to obtain soil hydraulic properties from easily measurable or readily available soil properties via pedo-transfer functions (PTFs), few articles provide guidance for obtaining soil hydraulic properties over a wide range of geoclimatic and regional data availability contexts. The aim of this study is, therefore, to develop guidelines and an associated spatially referenced toolbox, NB_PTFs, to speed the process of acquiring sensible soil hydraulic properties for different geoclimatic and data-rich/sparse regions. The guide compiles available information about soil hydraulic properties, as well as a large number (151) of PTFs, not collated in any other guidance to date. NB_PTFs is an open-source ArcGIS toolbox which allows users to quickly get values, graphs and spatial distributions of soil hydraulic properties. The soil hydraulic properties, obtained using the guide and the toolbox, can be used as inputs for various models amongst other purposes. To demonstrate the use of the guidelines and the toolbox in different geoclimatic and data-availability contexts, the paper presents two case studies: the Vietnamese Mekong Delta and New Zealand Hurunui catchment. The Vietnamese Mekong Delta shows the use of these guidelines in a tropical, flat location with limited information on soil physical, chemical and hydraulic properties. The Hurunui catchment represents a case study for a semi-arid and hilly area in an area with detailed soil information.

A Numerical Study for Tropical Cyclone Atsani (2020) Past Offshore of Southern Taiwan under Topographic Influences

Tropical Cyclone Atsani occurred in late October 2020 and moved westward offshore south of Taiwan. During its offshore passage, the cyclone deflects northward as it closes to the southern end of Taiwan. A global model MPAS at a multi-resolution of 60-15-3-km is applied to explore the track responses of Atsani and identify the topographic effects of the Central Mountain Range (CMR) on the cyclone circulation and the associated track deflection. With a 3-km resolution targeted at the Taiwan area, the cyclone track deflection can be reasonably simulated, with more sensitivity to physics schemes and dynamic vortex initialization and less sensitivity to initial environmental perturbations. When the Taiwan terrain is removed, the cyclone indeed deflects more northward earlier, in particular for simulations with a stronger cyclone that tends to generate stronger east-west wind asymmetry in the absence of the terrain. Idealized simulations with a regional model WRF at 3-km resolution are also utilized to contrast the track deflection of different departing cyclones, similar to the real case. It was found that northward deflection will be induced near south of the CMR-like terrain for both stronger and weaker westbound cyclones departing at different latitudes south of the terrain. We have explained why a further northward track at earlier stages is induced in the absence of the terrain effects in regard to model initial states. In both real and idealized cases, the track deflection of the cyclone moving around the terrain is dominated by the wavenumber-one horizontal potential vorticity (PV) advection that is somewhat offset by both vertical PV advection and differential diabatic heating.

A deep learning-based hybrid model of global terrestrial evaporation

Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, E_t) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress (S_t), i.e., the reduction of E_t from its theoretical maximum. Then, we embed the new S_t formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the S_t formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate S_t and E globally. The proposed framework may be extended to improve the estimation of E in Earth System Models and enhance our understanding of this crucial climatic variable.

Global evaporation is a key climatic process that remains highly uncertain. Here, the authors shed light on this process with a novel hybrid model that integrates a deep learning representation of ecosystem stress within a physics-based framework.

Evaluation of WRF Cumulus Parameterization Schemes for the Hot Climate of Sudan Emphasizing Crop Growing Seasons

High spatiotemporal resolution climate data are essential for climate-related impact studies. The Weather Research and Forecasting (WRF) model is widely used to downscale climate data for different regions with regional-specific physics configurations. This study aimed to identify robust configurations of the WRF model, especially cumulus parameterization schemes, for different climatic zones of Sudan. We focused on wet season (June–September) rainfall and dry season (November–February) temperature, which are determinants of summer crop and irrigated wheat yields, respectively. Downscaling experiments were carried out to compare the following schemes: Betts–Miller–Janjic (BMJ), improved Kain–Fritch (KFT), modified Tiedtke (TDK), and Grell–Freitas (GF). Results revealed that the BMJ performed better for wet season rainfall in the hyper-arid and arid zones; KFT performed better for rainfall in July and August in the semi-arid zone where most summer crops are cultivated. For dry season temperature, the BMJ and TDK outperformed the other schemes in all three zones, except that the GF performed best for the minimum temperature in December and January in the arid zone, where irrigated wheat is produced, and in the semi-arid zone. Specific parameterization schemes therefore need to be selected for specific seasons and climatic zones of Sudan.

Simulation of the Air Quality in Southern California, USA in July and October of the Year 2018

A numerical investigation of the air quality in Southern California, USA in the year 2018 is presented using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). In July, a heat wave occurred, and in October, Santa Ana conditions prevailed; these conditions and their impact on air quality are the scope of the present numerical study.The high spatial resolution in the simulation includes two nested domains of 1 km and 3 km, respectively. Local climate zones land use categories are combined with the complex urban model building effect parameterization coupled with the building energy model (BEP+BEM) and the detailed MOZCART-T1 chemical reaction mechanism, which is the MOZART-T1 mechanism for trace gases with GOCART aerosols. Thus, the model is suitable to compare simulation results to in situ and satellite measurements of O3, NO2, CH4, and CO. The meteorology is captured well by the model. Comparison of simulation results with observations shows a good agreement of NO2 and ozone, whereas CO mixing ratios are generally underestimated. This hints at missing emissions in the 2017 National Emissions Inventory (NEI) dataset. Both the heat wave and the Santa Ana winds increase the air pollution with gas-phase species in Los Angeles. In both cases, nighttime boundary layer heights are small, which causes emissions to reside near the ground. During Santa Ana winds, NOx removal on aerosols is reduced. Methane mixing ratios are modeled very well at most stations in Los Angeles, but predictions of low emissions near the University of California cause inaccuracies at that location. Modeled and observed PM2.5 agree well on low-pollution days, but high-pollution events are generally missed by the model. During the heat wave, both modeled and observed PM2.5 concentrations exceed the recommended NAAQS National Ambient Air Quality Standards value of 12.5 g/m3. The present modeling approach serves as a base for the study and prediction of special weather events and their impact on air pollution.

Evaluating the Impact of Planetary Boundary Layer, Land Surface Model, and Microphysics Parameterization Schemes on Simulated GOES-16 Water Vapor Brightness Temperatures

The impact of several land surface models (LSMs) and microphysics (MP), planetary boundary layer (PBL), and surface layer schemes on the accuracy of simulated brightness temperatures (BTs) from water vapor (WV) sensitive bands was examined via comparison with observations from the GOES-16 Advanced Baseline Imager. Nine parameterization configurations were evaluated. Analysis revealed that, compared to the Thompson MP scheme, the National Severe Storms Laboratory MP scheme produced lower simulated WV BTs in the upper troposphere but higher WV BTs in the middle and lower troposphere. The configuration with the Geophysical Fluid Dynamics Laboratory MP and hybrid eddy-diffusivity mass-flux (EDMF) PBL instead of Mellor–Yamada–Nakanishi–Niino (MYNN) PBL produced higher BTs. Yet, changing the PBL from MYNN to Shin–Hong or EDMF reduced the simulated WV BTs. Changing the LSM from Noah to RUC also resulted in lower simulated WV BTs, which were further enhanced with the MYNN surface layer instead of the GFS. The location and orientation of upper-level jet streams and troughs was assessed using the location of WV gradient objects. Every configuration had an increased translation speed compared to the observations, as forecast WV gradient objects were west of the observation objects early in the forecast and then east later in the forecast.

Valley–Mountain Circulation Associated with the Diurnal Cycle of Precipitation in the Tropical Andes (Santa River Basin, Peru)

The Cordillera Blanca (central Andes of Peru) represents the largest concentration of tropical glaciers in the world. The atmospheric processes related to precipitations are still scarcely studied in this region. The main objective of this study is to understand the atmospheric processes of interaction between local and regional scales controlling the diurnal cycle of precipitation over the Santa River basin located between the Cordillera Blanca and the Cordillera Negra. The rainy season (December–March) of 2012–2013 is chosen to perform simulations with the WRF (Weather Research and Forecasting) model, with two domains at 6 km (WRF-6 km) and 2 km (WRF-2 km) horizontal resolutions, forced by ERA5. WRF-2 km precipitation shows a clear improvement over WRF-6 km in terms of the daily mean and diurnal cycle, compared to in situ observations. WRF-2 km shows that the moisture from the Pacific Ocean is a key process modulating the diurnal cycle of precipitation over the Santa River basin in interaction with moisture fluxes from the Amazon basin. In particular, a channeling thermally orographic flow is described as controlling the afternoon precipitation along the Santa valley. In addition, in the highest parts of the Santa River basin (in both cordilleras) and the southern part, maximum precipitation occurs earlier than the lowest parts and the bottom of the valley in the central part of the basin, associated with the intensification of the channeling flow by upslope cross-valley winds during mid-afternoon and its decrease during late afternoon/early night.

A Methodology to Generate Integrated Land Cover Data for Land Surface Model by Improving Dempster-Shafer Theory

Land cover type is a key parameter for simulating surface processes in many land surface models (LSMs). Currently, the widely used global remote sensing land cover products cannot meet the requirements of LSMs for classification systems, physical definition, data accuracy, and space-time resolution. Here, a new fusion method was proposed to generate land cover data for LSMs by fusing multi-source remote sensing land cover data, which was based on improving Dempster-Shafer evidence theory with mathematical models and knowledge rules optimization. The new method has the ability to deal with seriously disagreement information, thereby improving the robustness of the theory. The results showed the new method can reduce the disagreement between input data and realized the conversion of multiple land cover classification systems to into a single land cover classification system. China Fusion Land Cover data (CFLC) in 2015 generated by the new method maintained the classification accuracy of the China land use map (CNLULC), which is based on visual image interpretation and further enriched land cover classes of input data. Compared with Geo-Wiki observations in 2015, the overall accuracy for CFLC is higher than other two global land cover data. Compared with the observations, the 0–10 cm soil moisture simulated by the CFLC in Noah–MP LSM during the growing season in 2014 had better performance than that simulated by initial land cover data and MODIS land cover data. Our new method is highly portable and generalizable to generate higher quality land cover data with a specific land cover classification system for LSMs by fusing multiple land cover data, providing a new approach to land cover mapping for LSMs.

Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage

Accurate and timely estimates of groundwater storage changes are critical to the sustainable management of aquifers worldwide, but are hindered by the lack of in-situ groundwater measurements in most regions. Hydrologic remote sensing measurements provide a potential pathway to quantify groundwater storage changes by closing the water balance, but the degree to which remote sensing data can accurately estimate groundwater storage changes is unclear. In this study, we quantified groundwater storage changes in California's Central Valley at two spatial scales for the period 2002 through 2020 using remote sensing data and an ensemble water balance method. To evaluate performance, we compared estimates of groundwater storage changes to three independent estimates: GRACE satellite data, groundwater wells and a groundwater flow model. Results suggest evapotranspiration has the highest uncertainty among water balance components, while precipitation has the lowest. We found that remote sensing-based groundwater storage estimates correlated well with independent estimates; annual trends during droughts fall within 15% of trends calculated using wells and groundwater models within the Central Valley. Remote sensing-based estimates also reliably estimated the long-term trend, seasonality, and rate of groundwater depletion during major drought events. Additionally, our study suggests that the proposed method estimate changes in groundwater at sub-annual latencies, which is not currently possible using other methods. The findings have implications for improving the understanding of aquifer dynamics and can inform regional water managers about the status of groundwater systems during droughts.

Characteristics of Swell-like Waves in the East Coast of Korea Using Atmospheric and Wave Hindcast Data

The long-term trend of swell-like waves invading the east coast of Korea was identified by using observations and hindcast data from 1979 to 2016. We defined a swell-like wave as a wave with a height of 2 m and a peak period of 10 s on the basis of a literature review of human casualties and property damage in the region. In total, 179 swell-like wave cases were detected from 1979 to 2016, with 132 cases caused by extratropical cyclones (ETCs). The track density analysis indicated that the ETCs were mainly generated on the east coast of China, over the East/Japan Sea, and over the Kuroshio-Oyashio extension region and then moved northeast. This reflects the prevailing wind direction, which was the most significant factor in generating the swell-like waves. The number of swell-like waves has been significantly increasing since the 2000s. This increasing trend of swell-like waves is linked with the synoptic eddy activity with a correlation of 0.53. They were associated with the reversed meridional gradient of surface air temperature and the consequent negative vertical wind shear anomaly near 40° N.

Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review

The correction of Soil Moisture (SM) estimates in Land Surface Models (LSMs) is considered essential for improving the performance of numerical weather forecasting and hydrologic models used in weather and climate studies. Along with surface screen-level variables, the satellite data, including Brightness Temperature (BT) from passive microwave sensors, and retrieved SM from active, passive, or combined active–passive sensor products have been used as two critical inputs in improvements of the LSM. The present study reviewed the current status in correcting LSM SM estimates, evaluating the results with in situ measurements. Based on findings from previous studies, a detailed analysis of related issues in the assimilation of SM in LSM, including bias correction of satellite data, applied LSMs and in situ observations, input data from various satellite sensors, sources of errors, calibration (both LSM and radiative transfer model), are discussed. Moreover, assimilation approaches are compared, and considerations for assimilation implementation are presented. A quantitative representation of results from the literature review, including ranges and variability of improvements in LSMs due to assimilation, are analyzed for both surface and root zone SM. A direction for future studies is then presented.

7 February Chamoli (Uttarakhand, India) Rock-Ice Avalanche Disaster: Model-Simulated Prevailing Meteorological Conditions

The present study aims to analyze the high-resolution model-simulated meteorological conditions during the Chamoli rock-ice avalanche event, which occurred on 7 February 2021 in the Chamoli district of Uttarakhand, India (30.37° N, 79.73° E). The Weather Research and Forecasting (WRF) model is used to simulate the spatiotemporal distribution of meteorological variables pre- and post-event. The numerical simulations are carried out over two fine resolution nested model domains covering the Uttarakhand region over a period of 2 weeks (2 February to 13 February 2021). The model-simulated meteorological variables, e.g., air temperature, surface temperature, turbulent heat flux, radiative fluxes, heat and momentum transfer coefficients, specific humidity and upper wind patterns, were found to show significant departures from their usual patterns starting from 72 h until a few hours before the rock-ice avalanche event. The average 2 m air and surface temperatures near the avalanche site during the 48 h before the event were found to be much lower than the average temperatures post-event. In-situ observations and the ERA5-Land dataset also confirm these findings. The total turbulent heat flux mostly remained downward (negative) in the 72 h before the event and was found to have an exceptionally large negative value a few hours before the rock-ice avalanche event. The model-simulated rainfall and Global Precipitation Measurement (GPM, IMERG)-derived rainfall suggest that the part of the Himalayan region falling in the simulation domain received a significant amount of rainfall on 4 February, around 48 h prior to the event, while the rest of the days pre- and post-event were mostly dry. The results presented here might be helpful in further studies to identify the possible trigger factors of this event.