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Methane (CH4) is a potent greenhouse gas (GHG) with atmospheric concentrations that have nearly tripled since pre‐industrial times. Wetlands account for a large share of global CH4 emissions, yet the magnitude and factors controlling CH4 fluxes in tidal wetlands remain uncertain. We synthesized CH4 flux data from 100 chamber and 9 eddy covariance (EC) sites across tidal marshes in the conterminous United States to assess controlling factors and improve predictions of CH4 emissions. This effort included creating an open‐source database of chamber‐based GHG fluxes (https://doi.org/10.25573/serc.14227085). Annual fluxes across chamber and EC sites averaged 26 ± 53 g CH4 m−2 year−1, with a median of 3.9 g CH4 m−2 year−1, and only 25% of sites exceeding 18 g CH4 m−2 year−1. The highest fluxes were observed at fresh‐oligohaline sites with daily maximum temperature normals (MATmax) above 25.6°C. These were followed by frequently inundated low and mid‐fresh‐oligohaline marshes with MATmax ≤25.6°C, and mesohaline sites with MATmax >19°C. Quantile regressions of paired chamber CH4 flux and porewater biogeochemistry revealed that the 90th percentile of fluxes fell below 5 ± 3 nmol m−2 s−1 at sulfate concentrations >4.7 ± 0.6 mM, porewater salinity >21 ± 2 psu, or surface water salinity >15 ± 3 psu. Across sites, salinity was the dominant predictor of annual CH4 fluxes, while within sites, temperature, gross primary productivity (GPP), and tidal height controlled variability at diel and seasonal scales. At the diel scale, GPP preceded temperature in importance for predicting CH4 flux changes, while the opposite was observed at the seasonal scale. Water levels influenced the timing and pathway of diel CH4 fluxes, with pulsed releases of stored CH4 at low to rising tide. This study provides data and methods to improve tidal marsh CH4 emission estimates, support blue carbon assessments, and refine national and global GHG inventories. [ABSTRACT FROM AUTHOR]

Accurate precipitation estimates are crucial for various hydrological and environmental applications. This study presents a comprehensive evaluation of three widely used satellite-based precipitation datasets (SPDs)—PERSIANN, CHIRPS, and MERRA—and a monthly reanalysis dataset—TERRA—that include data from across the contiguous United States (CONUS) and Hawaii, at daily, monthly, and yearly timescales. We present the performance of these SPDs using ground-based observations maintained by the USGS (United States Geological Survey). We employ evaluation metrics, such as the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), to identify optimal SPDs. Our findings reveal that MERRA outperforms PERSIANN and CHIRPS on a daily scale, while CHIRPS is the best-performing dataset on a monthly scale. However, all datasets show limitations in accurately estimating absolute amount of precipitation totals. The spatial analysis highlights regional variations in the datasets' performance, with MERRA consistently performing well across most regions, while CHIRPS and PERSIANN show strengths in specific areas and months. We also observe a consistent seasonal pattern in the performance of all datasets. This study contributes to the growing body of knowledge on satellite precipitation estimates and their applications, guiding the selection of suitable datasets based on the required temporal resolution and regional context. As such SPDs continue to evolve, ongoing evaluation and improvement efforts are crucial to enhance their reliability and support informed decision-making in various fields, including water resource management, agricultural planning, and climate studies. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a key process in the eco-hydrological cycle of a basin and a reliable indicator of climate change. However, the spatiotemporal alterations of ET in the contiguous United States (CONUS) over the recent two decades remain largely uncertain. In this study, we used the recently proposed Priestley–Taylor (PT)-SinRH model to estimate the ET of CONUS during 2001–2022 based on satellite and reanalysis data. The results showed that the PT-SinRH model yielded superior performance at eddy covariance (EC) sites, and the root-mean-square error (RMSE) ranged from 6.0 to 33.5 W/m2, the Kling–Gupta efficiency (KGE) ranged from 0.22 to 0.66. The annual mean value of ET in CONUS from 2001 to 2022, estimated by the PT-SinRH model, was 42.54 W/m2, and the spatial pattern of seasonal and annual ET variations increased from west to east. From 2001 to 2022, seasonal and annual ET of CONUS showed linear trends, with an average increase of 0.76 W/m2/da (p < 0.05). The ET in the east of CONUS exhibited a rate of increase at 1.45 W/m2/da, and the ET in the west of CONUS exhibited a rate of increase at 0.42 W/m2/da (p < 0.05). Importantly, our analysis of ET trends highlights that the change of precipitation (P) and normalized difference vegetation index (NDVI) exerts a significant impact on the change of ET over CONUS. [ABSTRACT FROM AUTHOR]

Historically, the precipitation trend over the past few decades in the Contiguous United States (CONUS) exhibits a "Dry‐West Wet‐East" pattern; this is manifested by recent droughts/floods in the western/eastern US. However, it remains elusive what atmospheric phenomenon has potentially driven such a remarkable, and impactful precipitation pattern. Here we found that a coupled climate mode—the Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by using Atmospheric Model Intercomparison Project simulations available from the Coupled Model Intercomparison Project Phase 6. Plain Language Summary: Historically, the Contiguous United States (CONUS) was parched in the West and soaked in the East. Despite the importance of this distinct spatial pattern of precipitation variability, the drivers of this hydroclimatic phenomenon remain elusive. Therefore, there is big knowledge gap in understanding the zonal precipitation pattern during the historical period. Here we find that the Pacific Meridional Mode (PMM) can exert strong impacts on the zonal precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and the precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by the Atmospheric Model Intercomparison Project experiments of the Coupled Model Intercomparison Project (Phase 6). Our research results explain in large part the zonal precipitation pattern ("Dry‐West Wet‐East") in the CONUS, and perhaps more importantly, the viability of the PMM being used as a potential predictor/indicator for summertime precipitation over the U.S. Key Points: The Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer seasonThe modulation on precipitation is manifested as a zonal dipole pattern, tied to teleconnections associated PMM sea‐surface temperature anomaliesThe physical mechanisms based on observations were supported by CMIP6 climate model simulations [ABSTRACT FROM AUTHOR]

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash–Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from −0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series. [ABSTRACT FROM AUTHOR]

In the past century, local-scale warming caused by a strengthening urban heat island effect has brought inevitable systematic bias to observational data from surface weather stations located in or near urban areas. In this study, the land use situation around U.S. Climate Reference Network (USCRN) stations was used as a reference for rural station selection; stations with similar environmental conditions in the U.S. Historical Climatology Network (USHCN) were selected as reference stations using a machine learning method, and then the maximum surface air temperature (Tmax) series, minimum surface air temperature (Tmin) series and mean surface air temperature (Tmean) series of rural stations during 1921–2020 were compared with those for all nearby stations (including both rural and urban stations) to evaluate urbanization effects in the USHCN observation data series of the contiguous United States, which can be regarded as urbanization bias contained in the latest homogenized USHCN observation data. The results showed that the urbanization effect on the Tmean trend of USHCN stations is 0.002 °C dec−1, and the urbanization contribution is 35%, indicating that urbanization around USHCN stations has led to at least one-third of the overall warming recorded at USHCN stations over the last one hundred years. The urbanization effects on Tmax and Tmin trends of USHCN stations are −0.015 °C dec−1 and 0.013 °C dec−1, respectively, and the urbanization contribution for Tmin is 34%. These results have significance for understanding the systematic bias in USHCN temperature data, and they provide a reference for subsequent studies on data correction and climate change monitoring. [ABSTRACT FROM AUTHOR]

Abstract Historically, the precipitation trend over the past few decades in the Contiguous United States (CONUS) exhibits a “Dry‐West Wet‐East” pattern; this is manifested by recent droughts/floods in the western/eastern US. However, it remains elusive what atmospheric phenomenon has potentially driven such a remarkable, and impactful precipitation pattern. Here we found that a coupled climate mode—the Pacific Meridional Mode (PMM) exerted strong impacts on the precipitation pattern over the CONUS during the summer season. We discovered a significant association between the PMM index and precipitation across the majority of the CONUS; this was manifested as a zonal dipole pattern—negative correlations in the western U.S. along with positive correlations in the eastern and central U.S. Overall, the physical mechanisms based on observations were supported by using Atmospheric Model Intercomparison Project simulations available from the Coupled Model Intercomparison Project Phase 6.

Accurate precipitation estimates are crucial for various hydrological and environmental applications. This study presents a comprehensive evaluation of three widely used satellite-based precipitation datasets (SPDs)—PERSIANN, CHIRPS, and MERRA—and a monthly reanalysis dataset—TERRA—that include data from across the contiguous United States (CONUS) and Hawaii, at daily, monthly, and yearly timescales. We present the performance of these SPDs using ground-based observations maintained by the USGS (United States Geological Survey). We employ evaluation metrics, such as the coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE), to identify optimal SPDs. Our findings reveal that MERRA outperforms PERSIANN and CHIRPS on a daily scale, while CHIRPS is the best-performing dataset on a monthly scale. However, all datasets show limitations in accurately estimating absolute amount of precipitation totals. The spatial analysis highlights regional variations in the datasets’ performance, with MERRA consistently performing well across most regions, while CHIRPS and PERSIANN show strengths in specific areas and months. We also observe a consistent seasonal pattern in the performance of all datasets. This study contributes to the growing body of knowledge on satellite precipitation estimates and their applications, guiding the selection of suitable datasets based on the required temporal resolution and regional context. As such SPDs continue to evolve, ongoing evaluation and improvement efforts are crucial to enhance their reliability and support informed decision-making in various fields, including water resource management, agricultural planning, and climate studies.

The reliable prediction of streamflow is crucial for various water resources, environmental, and ecosystem applications. The current study employs a complex networks-based approach for the prediction of streamflow. The approach consists of three major steps: (1) the formation of a network using streamflow time series; (2) the calculation of the clustering coefficient (CC) as a network measure; and (3) the use of a clustering coefficient-based nearest neighbor search procedure for streamflow prediction. For network construction, each timestep is considered as a node and the existence of link between any node pair is identified based on the difference (distance) between the streamflow values of the nodes. Different distance threshold values are used to identify the critical distance threshold to form the network. The complex networks-based approach is implemented for the prediction of daily streamflow at 142 stations in the contiguous United States. The prediction accuracy is quantified using three statistical measures: correlation coefficient (R), normalized root mean square error (NRMSE), and Nash–Sutcliffe efficiency (NSE). The influence of the number of neighbors on the prediction accuracy is also investigated. The results, obtained with the critical distance threshold, reveal that the clustering coefficients for the 142 stations range from 0.799 to 0.999. Overall, the prediction approach yields reasonably good results for all 142 stations, with R values ranging from 0.05 to 0.99, NRMSE values ranging from 0.1 to 12.3, and the NSE values ranging from −0.89 to 0.99. An attempt is also made to examine the relationship between prediction accuracy and the catchment characteristics/streamflow statistical properties (drainage area, mean flow, coefficient of variation of flow). The results suggest that the prediction accuracy does not have much of a relationship with the drainage area and the mean streamflow values, but with the coefficient of variation of flow. The outcomes from this study are certainly promising regarding the application of complex networks-based concepts for the prediction of streamflow (and other hydrologic) time series.

Agricultural drought assessment is based on soil moisture deficit during the plant-growing season. The available long-term in situ soil moisture data can be used to evaluate the drought indices' performance. Drought indices have different sensitivities to various climates and vegetation cover types. Moreover, little research has been carried out on the sensitivity to soil moisture in specific soil layers. Comparing the performance of drought indices in different environments enables us to use and construct an effective drought index for drought delineation. We used soil moisture data from 2000 to 2021 to compare the performance of satellite image-derived drought indices (VCI, NLSWI, TCI, VHI, and NVSWI) in different climates, vegetation cover types, and soil layers over the Contiguous United States at monthly intervals and a 1000 m spatial resolution. The performances of drought indices based on vegetation status showed little differences in the different soil layers. In contrast, drought indices based on single-temperature conditions performed better in the 0–20 cm soil layer than in the deeper soil layer. The integrated index might not improve the correlation in all soil layers compared with single-condition indices under some environments. In general, while the optimal drought index might vary in different environments, the VHI performed most efficiently in most cases. This research has the potential to improve our understanding of drought monitoring in large regions with different climates, vegetation cover types, and soil layers. [ABSTRACT FROM AUTHOR]

In the past century, local-scale warming caused by a strengthening urban heat island effect has brought inevitable systematic bias to observational data from surface weather stations located in or near urban areas. In this study, the land use situation around U.S. Climate Reference Network (USCRN) stations was used as a reference for rural station selection; stations with similar environmental conditions in the U.S. Historical Climatology Network (USHCN) were selected as reference stations using a machine learning method, and then the maximum surface air temperature (Tmax) series, minimum surface air temperature (Tmin) series and mean surface air temperature (Tmean) series of rural stations during 1921–2020 were compared with those for all nearby stations (including both rural and urban stations) to evaluate urbanization effects in the USHCN observation data series of the contiguous United States, which can be regarded as urbanization bias contained in the latest homogenized USHCN observation data. The results showed that the urbanization effect on the Tmean trend of USHCN stations is 0.002 °C dec−1, and the urbanization contribution is 35%, indicating that urbanization around USHCN stations has led to at least one-third of the overall warming recorded at USHCN stations over the last one hundred years. The urbanization effects on Tmax and Tmin trends of USHCN stations are −0.015 °C dec−1 and 0.013 °C dec−1, respectively, and the urbanization contribution for Tmin is 34%. These results have significance for understanding the systematic bias in USHCN temperature data, and they provide a reference for subsequent studies on data correction and climate change monitoring.

Identifying regions that mediate regional propagation of atmospheric perturbations is important to assessing the susceptibility and resilience of complex hydroclimate systems. Detecting the regional gateways through causal inference, can help unravel the interplay of physical processes and inform projections of future changes. In this study, we characterize the causal interactions among nine climate regions in the contiguous United States using long‐term (1901–2018) precipitation data. The constructed causal networks reveal the cross‐regional propagation of precipitation perturbations. Results show that the Ohio Valley region acts as an atmospheric gateway for precipitation and moisture transport in the U.S., which is largely regulated by the regional convective uplift. The findings have implications for improving predicative capacity of hydroclimate modeling of regional precipitation. Plain Language Summary: Successful detection of causality in complex systems is important to unraveling the underlying mechanisms of system dynamics. The dynamic interactions in Earth's climate system are often nonlinear, weakly or moderately coupled, and essentially non‐separable, which renders conventional approaches of causal inference, such as statistical correlation or Granger causality, infeasible or ineffective. Here we applied the convergent cross mapping method to detect causal influence among different climate regions in the contiguous U.S. in response to precipitation perturbations. The results of our study show that the Ohio Valley region, as an atmospheric convergence zone, acts as a regional gateway and mediator for the long‐term precipitation perturbations in the U.S. The temporal evolution of causal effect and susceptibility exhibits superposition of climate variability at various time scales, highlighting the impact of prominent climate variabilities such as El Niño–Southern Oscillation on the dynamics of causality. Key Points: We use the convergent cross mapping algorithm (CCM), based on embedding theory, for causality inference in this studyCCM is used to detect causal influence in precipitation perturbations among different climate regions of USThe Ohio Valley region emerges as a causal gateway of moisture transport and propagation of regional precipitation anomalies in the US [ABSTRACT FROM AUTHOR]

Each year, tropical cyclones (TCs) cause significant damage to both society and the environment through multiple hazards. While extensive research has focused on individual TC primary hazards, there is a gap in the comprehensive assessment of multiple TC-related hazards using a large number of TCs, likely due to limitations in methodology and data when identifying complex hazard interrelationships. To address this challenge, we developed novel methods using a rule-based natural language processing (NLP) approach to extract TC-related weather-hazardous events from official hazard records, of which narratives provide detailed characterization of TC multi-hazard interrelationships. We applied this method on TCs originating from the North Atlantic and East Pacific Oceans affecting the Contiguous United States. The results showed that the NLP methods, especially the large language models, can identify TC names from the texts with reasonable precision. A total of 21 488 events related to 179 TCs were identified from 2007 to 2022. These events encompassed 31 types of hazards, with precipitation and flooding being the most frequent and causing the most direct fatalities, while wind hazards cause the most direct property damage. Although TC wind and storm surges are typically concentrated within 100–200 km of the storm track, hazards can occur hundreds of kilometers away and even after the storm’s dissipation. During this period, 2033 counties experienced at least one TC-related event, impacting not only the eastern seaboard but also inland areas in central and southwestern regions. These findings underscore the importance of a multi-hazard perspective on TCs, enhancing hazard awareness and informing decision-making.

The development of a high-quality wildfire occurrence model is an essential component in mapping present wildfire risk, and in projecting future wildfire dynamics with climate and land-use change. Here, we develop a new model for predicting the daily probability of wildfire occurrence at 0.1° (∼10 km) spatial resolution by adapting a generalised linear modelling (GLM) approach to include improvements to the variable selection procedure, identification of the range over which specific predictors are influential, and the minimisation of compression, applied in an ensemble of model runs. We develop and test the model using data from the contiguous United States. The ensemble performed well in predicting the mean geospatial patterns of fire occurrence, the interannual variability in the number of fires, and the regional variation in the seasonal cycle of wildfire. Model runs gave an area under the receiver operating characteristic curve (AUC) of 0.85–0.88, indicating good predictive power. The ensemble of runs provides insight into the key predictors for wildfire occurrence in the contiguous United States. The methodology, though developed for the United States, is globally implementable.

Abstract Identifying regions that mediate regional propagation of atmospheric perturbations is important to assessing the susceptibility and resilience of complex hydroclimate systems. Detecting the regional gateways through causal inference, can help unravel the interplay of physical processes and inform projections of future changes. In this study, we characterize the causal interactions among nine climate regions in the contiguous United States using long‐term (1901–2018) precipitation data. The constructed causal networks reveal the cross‐regional propagation of precipitation perturbations. Results show that the Ohio Valley region acts as an atmospheric gateway for precipitation and moisture transport in the U.S., which is largely regulated by the regional convective uplift. The findings have implications for improving predicative capacity of hydroclimate modeling of regional precipitation.

Streamflow drought is a recurring challenge, and understanding spatiotemporal patterns of past droughts is needed to manage future water resources. We examined regional patterns in streamflow drought metrics and compared these metrics to low flow timing and magnitude using long‐term daily records for 555 minimally disturbed watersheds. For each streamgage, we calculated streamflow drought duration (number of days) and deficit (flow volume below a specified threshold) for each climate year (April 1–March 31). We identified drought using five thresholds (2%–30%) and two approaches: variable thresholds with unique values for each day of the year, and a fixed threshold based on all period‐of‐record flows. We then analyzed drought trends using the Mann‐Kendall test with persistence adjustment for 1921–2020, 1951–2020, and 1981–2020, and computed correlations between annual streamflow drought metrics and climate metrics using values from a monthly water balance model. Spatial patterns in drought metrics were consistent between variable and fixed approaches, though fixed threshold durations were typically longer and variable threshold deficits larger. High interannual variability in drought duration emerged in the central, interior west, and southwestern U.S., with high deficit variability in the interior west. Drought metrics were weakly correlated with low flow magnitude and timing, providing unique information. Drought duration and deficit increased in the southern and western U.S. for both 1951–2020 and 1981–2020, particularly using fixed thresholds, and paralleled trends in aridity. Projections of continued aridification for the southern and western U.S. may increase drought durations and deficits and intensify water availability impacts. Key Points: Streamflow drought metrics show similar spatial patterns using fixed and variable thresholds, with unique information versus 7‐day low flowStreamflow drought duration and deficit increased in the south and west U.S., with more increases when using fixed drought thresholdsStreamflow drought trends were correlated to aridity trends; continued west U.S. aridification may increase streamflow drought impacts [ABSTRACT FROM AUTHOR]

The predictions of extreme events by the Unified Forecast System (UFS) Coupled Model Prototype 5 of the National Centers for Environmental Prediction over the contiguous United States during boreal summer are assessed. The extreme events in precipitation and daily maximum and minimum surface air temperature in weeks 1–4 predictions are analyzed in the deterministic retrospective forecasts of UFS during 2011–2017. The spatial structures of the extreme events in precipitation are reasonably well predicted but with higher values. Although the predictions of the temperature are closer to observation over central and eastern parts of the US, the model fails to generate the extreme events over large western regions. There is no appreciable growth of forecast errors of extreme events during weeks 1–4. While the spatial correlation of the number of extreme events between the forecasts and observation is very low for precipitation and temperature, the correlation of the temperature per event is very high. The model is able to better predict the observed location and magnitude of temperature events whenever it can generate such events. The number of precipitation events in the forecasts is higher than in the observation but with less accuracy in location and magnitude. The influence of slowly varying modes related to El Niño-Southern Oscillation (ENSO), intraseasonal oscillation (ISO) and warming trend of the ocean on the extreme events are also studied. All three modes have enhancing influence on precipitation while only the ENSO mode enhances the maximum temperature events. The minimum temperature events are enhanced by ENSO and ISO but diminished by the warming trend. [ABSTRACT FROM AUTHOR]

Abstract Snow water equivalent (SWE), temperature, and precipitation biases and trends are evaluated in the atmosphere‐land simulations of the Energy Exascale Earth System Model version 1 (E3SMv1) in comparison to the Community Earth System Model version 2 (CESM2) and two other models using the ground measurement‐based University of Arizona (UA) snow product. SWE, temperature, and precipitation biases are highest in magnitude in the Western contiguous United States (CONUS). SWE errors are attributed to temperature and precipitation through multiple linear regressions of normalized errors, the coefficients of which represent the sensitivities to temperature and precipitation errors. SWE errors are more sensitive to temperature errors throughout the CONUS. Model SWE and temperature trends are generally opposite from UA product trends in the Western CONUS. SWE trend errors are also attributed to temperature and precipitation trend errors using multiple linear regressions of normalized trend errors. SWE trend errors are more sensitive to those of precipitation at higher elevations (>1,500 m) in the Western CONUS in these simulations. Thus, the sensitivity to temperature and precipitation differ for SWE errors and its trend errors. Furthermore, the SWE trend errors are more sensitive to temperature and precipitation in the atmosphere‐ocean coupled simulations in which the atmosphere‐land is coupled to an active ocean model. These results suggest that both errors in simulated temperature and precipitation contribute to SWE errors.

Snow water equivalent (SWE), temperature, and precipitation biases and trends are evaluated in the atmosphere‐land simulations of the Energy Exascale Earth System Model version 1 (E3SMv1) in comparison to the Community Earth System Model version 2 (CESM2) and two other models using the ground measurement‐based University of Arizona (UA) snow product. SWE, temperature, and precipitation biases are highest in magnitude in the Western contiguous United States (CONUS). SWE errors are attributed to temperature and precipitation through multiple linear regressions of normalized errors, the coefficients of which represent the sensitivities to temperature and precipitation errors. SWE errors are more sensitive to temperature errors throughout the CONUS. Model SWE and temperature trends are generally opposite from UA product trends in the Western CONUS. SWE trend errors are also attributed to temperature and precipitation trend errors using multiple linear regressions of normalized trend errors. SWE trend errors are more sensitive to those of precipitation at higher elevations (>1,500 m) in the Western CONUS in these simulations. Thus, the sensitivity to temperature and precipitation differ for SWE errors and its trend errors. Furthermore, the SWE trend errors are more sensitive to temperature and precipitation in the atmosphere‐ocean coupled simulations in which the atmosphere‐land is coupled to an active ocean model. These results suggest that both errors in simulated temperature and precipitation contribute to SWE errors. Plain Language Summary: Snow mass, temperature, and precipitation biases and trends are evaluated in atmosphere‐land only simulations forced by observed sea surface temperatures in the Department of Energy Earth system model (E3SMv1) in comparison to three other models. Snow mass, temperature, and precipitation biases are highest in magnitude in the Western contiguous U.S. (CONUS). Model snow mass and temperature trends are largely opposite those of observations in the Western CONUS. Snow mass errors are attributed to temperature and precipitation through multiple linear regressions of normalized errors errors, the coefficients of which represent the sensitivities to these quantities. Snow mass errors are more sensitive to temperature errors throughout the CONUS. Snow mass trend errors are also attributed to temperature and precipitation through multiple linear regressions of normalized trend errors. Snow mass trend errors are more sensitive to those of precipitation at higher elevations in the Western CONUS in these simulations. Also, the snow mass trend errors are more sensitive to temperature and precipitation in the atmosphere‐ocean coupled simulations in which the atmosphere‐land is coupled to an active ocean model. These results suggest that both errors in simulated temperature and precipitation contribute to snow mass errors. Key Points: Model snow water equivalent (SWE) errors in the atmosphere‐land simulations are more attributed to temperature than precipitation errorsModel SWE and temperature trends in the atmosphere‐land simulations are largely opposite those of observations in the Western contiguous United StatesModel SWE trend errors are more sensitive to temperature and precipitation trend errors in the atmosphere‐ocean simulations [ABSTRACT FROM AUTHOR]

• Rainfall leads to increased humidity and a subsequent end to the heatwave. • Post- heatwave periods witness a notable decrease in water storage. • Heatwave conclusions correspond with heightened rainfall and increased runoff. • Rainfall is more probable post- heatwave than in non– heatwave intervals. Extreme heat and drought conditions are affecting water availability in many regions worldwide, leading to negative impacts on human societies, agriculture, and ecosystems. However, current research lacks comprehensive spatiotemporal analysis examining the interplay between multiple hydrological factors and heatwave events, especially in the context of climate change. This research broadly pertains to understanding the dynamics of hydrological factors and their potential responses to heatwave during warm seasons across the contiguous United States for the period from 2003 to 2022. Utilizing data from the Global Land Data Assimilation System (GLDAS), we analyzed surface runoff, evapotranspiration (ET), precipitation, Groundwater Storage (GWS), Root Zone Soil Moisture (RZSM), and Total Water Storage (TWS) to discern annual patterns and the impacts of heatwave. The spatial patterns of heatwave highlighted a higher occurrence in the western, central, and northeastern U.S., with longer average durations in the western and south-central regions. These events are predominantly dry, characterized by low Relative Humidity (RH), except in the southeastern U.S., where heatwave coincide with high RH levels. Post-heatwave analysis indicated a reduction in GWS, TWS, RZSM, and ET, alongside an increase in surface runoff, RH, and precipitation. An in-depth examination of rainfall and temperature dynamics during heatwave revealed weak correlations between rainfall and temperature, as well as between rainfall and heatwave duration, highlighting the complex nature of these interactions. The study also found an enhanced probability of rainfall following heatwave, particularly in the eastern regions, drawing attention to the potential for increased flood risks post-heatwave. Our findings contribute to the growing body of knowledge on the impacts of heatwave on hydrological factors, providing valuable insights for climate change adaptation and water resource management strategies. [ABSTRACT FROM AUTHOR]

This study investigates municipal water use patterns across the Contiguous United States (CONUS). The objectives of this study are to explore temporal trends in water use, improve characterization of indoor and outdoor uses, and improve characterization of commercial, industrial, and institutional (CII) water use in cities across the CONUS. A comprehensive survey was conducted to compile monthly water use data from 126 municipalities for the period 2005–2017 with specific information about residential and CII water use categories. Changes in liters per capita per day and the CII to Residential water use ratio were related to climatic, urban‐geologic and socio‐economic variables. Results indicate an overall decreasing trend in municipal water uses with higher reductions achieved in residential sector. Both residential and CII water use exhibit high seasonality over an average year. The CII to Residential ratio increases with city population and is highest in cities in the Northeast Census region. Cities in South and West Census regions have high municipal water uses and highest reductions in annual per capita‐day water use. Cities in arid climate regions have the highest water uses, compared to other cities, due to landscape irrigation. April precipitation, annual vapor pressure deficit, number of employees in the manufacturing (or, other services except public administration) sector, total percentage of houses built before 1950 and total percentage of single‐family houses explain much of the variation in CII to Residential water use ratio across the CONUS. This study guides improved characterization of municipal water uses and water demand management strategies. Key Points: Total municipal water use in the contiguous U.S. has declined at median annual rate of 9.8 liters per capita per day from 2005 to 2017Water use reductions are highest among cities characterized by large population, arid climate and West Census region of the contiguous U.S.Five climatic, economic and urban‐geologic variables explain the commercial, industrial and institutional to residential water use ratio [ABSTRACT FROM AUTHOR]

Motivation: Freshwater insects comprise 60% of freshwater animal diversity; they are widely used to assess water quality, and they provide prey for numerous freshwater and terrestrial taxa. Our knowledge of the distribution of freshwater insect diversity in the USA is incomplete because we lack comprehensive, standardized data on their distributions and functional traits at the scale of the contiguous United States (CONUS). We fill this knowledge gap by presenting Freshwater insects CONUS: A database of freshwater insect occurrences and traits for the contiguous United States. This database includes 2.05 million occurrence records for 932 genera in the major freshwater insect orders, at 51,044 stream locations sampled between 2001 and 2018 by federal and state biological monitoring programmes. Compared with existing openaccess databases, we tripled the number of occurrence records and locations and added records for 118 genera. We also present life-history, dispersal, morphological and ecological traits and trait affinities (analogous to fuzzy-coded traits) for 1,007 stream insect genera, assembled from existing databases, reference books and the primary literature. We nearly doubled the number of traits for 11 trait groups and added traits for 180 genera that were not available from open-access databases. Our database, Freshwater insects CONUS, facilitates the mapping of freshwater insect taxonomic and functional diversity and, when paired with environmental data, will provide a powerful resource for quantifying how the environment shapes stream insect diversity and taxon-specific distributions. Main types of variables contained: Georeferenced occurrence records and traits for stream insects. Spatial location and grain: Contiguous United States at a grain of c. 1 m2. Time period and grain: Occurrence records from January 2001 to December 2018, with 1-day temporal resolution. Traits from January 1911 to December 2018. Major taxa and level of measurement: Genera from the orders Coleoptera, Diptera, Ephemeroptera, Hemiptera, Lepidoptera, Megaloptera, Neuroptera, Odonata, Plecoptera and Trichoptera. Software format: .csv. [ABSTRACT FROM AUTHOR]

Changes in postharvest storage conditions due to climate change can directly affect energy usage and food supply and quality. However, no study has assessed climate change impacts on postharvest storage conditions in different climate regions over the contiguous United States (CONUS), a major agricultural producer around the world. The goal of this study is to assess the impact of climate change on cold storage conditions for the highest grossing crop for each of the nine climate regions within the CONUS. Storage degree days (SDDs) accumulate when ambient temperatures increase relative to crop storage base temperatures. Changes in SDDs and winter subperiod length were calculated for each regional crop using historical climate data and 20 downscaled global climate model projections. All regions project significant increases in SDD accumulation and decreases in winter subperiod length when compared with the historical reference period (1979–2005). Between years 2020 and 2080, Northwest and Northeast regions' apples will be impacted most by SDD accumulation with yearly increases between 261 and 1004 SDDs. Between years 2020 and 2080, Midwest regions' potatoes are projected to lose the most days of winter (24–39 days), and Southeast regions' peanuts will experience the greatest decrease in winter length (17–23%). Increases in SDD accumulation and decreases in winter length will have direct implications on future food supply and storage costs. This study is the first comprehensive analysis of climate change impacts on the storage conditions for agricultural commodities over heterogenous climate conditions at national scale, providing useful information for long-term agricultural storage planning. [ABSTRACT FROM AUTHOR]

Future climate change predictions by global climate models or earth system models diverge significantly, most likely due to their different cloud responses to global warming. There is an uncertainty as to how the cloud frequency (or cloud fraction) and height will change, in turn, affecting the sign, and amount of cloud feedbacks. While satellite observations have been very useful in augmenting information on clouds, it is mostly related to cloud tops, and there is a lack of information on cloud base height (CBH). In this study, a unique record of CBH information was collected at 706 Automated Surface Observing System (ASOS) ceilometer stations to evaluate the ability of the North American Regional Reanalysis (NARR) model to correctly simulate similar information. It was found that NARR can capture the geographical distribution and the seasonal variation of CBH and cloud base frequency (CBF). On average, the CBF values of NARR were 7% fewer and the CBH values of NARR were 631 m lower than those from observations that span the distance from the surface to 7,600 m. NARR simulates CBH better in arid area in the west of the contiguous United States (CONUS) than in humid areas, where NARR frequently predicted cloud bases too low compared with observations. In the west coast area of the CONUS, the discontinuity between high cloud bases over arid inland areas and low marine cloud layers from the Pacific Ocean over coastal areas produced especially large deviations between the NARR‐simulated and ASOS‐observed CBHs. [ABSTRACT FROM AUTHOR]

• Precipitation annual maxima estimated from NEXRAD are evaluated over the CONUS. • Accuracy of radar-based estimates varies with magnitude and duration. • Uncertainty in radar-based IDF curves exhibits climate dependence. The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002–2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-h) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24 h). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes. [ABSTRACT FROM AUTHOR]

• Quantified causal interactions between temperature and geopotential height. • Urban thermal environment strongly regulated by local pressure systems. • Emergence of teleconnections largely due to westerly circulations. Excessive warming is the source of many environmental challenges in urban areas. Finding causal interactions between urban ambient temperature and other meteorological variables will greatly facilitate our understanding of the underlying mechanisms of urban heat. In this study, we use the data of 2-m air temperature and 500 hPa geopotential height (GPH) in 520 cities in the contiguous United States (CONUS), from 2016 to 2022, to detect their local and nonlocal causal interactions based on the convergent cross-mapping method. For local (within the same city) interactions between temperature and GPH, there are hubs of strong causal interactions in the northern Appalachian Mountain and inland southwestern CONUS, whereas the leeward side of the southern Rocky Mountains exhibit low causation due to the baroclinic in this area. The nonlocal causal networks exhibit potential long-range connections (teleconnections), largely attributable to the influence of the upstream and downstream westerly circulations on local atmospheric variables. This study can be informative to stakeholders in design sustainable countermeasures to mitigate excessive heat in urban areas. [ABSTRACT FROM AUTHOR]

Effective natural resource planning depends on understanding the prevalence of runoff generating processes. Within a specific area of interest, this demands reproducible, straightforward information that can complement available local data and can orient and guide stakeholders with diverse training and backgrounds. To address this demand within the contiguous United States (CONUS), we characterized and mapped the predominance of two primary runoff generating processes: infiltration‐excess and saturation‐excess runoff (IE vs. SE, respectively). Specifically, we constructed a gap‐filled grid of surficial saturated hydraulic conductivity using the Soil Survey Geographic and State Soil Geographic soils databases. We then compared surficial saturated hydraulic conductivity values with 1‐hr rainfall‐frequency estimates across a range of return intervals derived from CONUS‐scale random forest models. This assessment of the prevalence of IE versus SE runoff also incorporated a simple uncertainty analysis, as well as a case study of how the approach could be used to evaluate future alterations in runoff processes resulting from climate change. We found a low likelihood of IE runoff on undisturbed soils over much of CONUS for 1‐hr storms with return intervals <5 years. Conversely, IE runoff is most likely in the Central United States (i.e., Texas, Louisiana, Kansas, Missouri, Iowa, Nebraska, and Western South Dakota), and the relative predominance of runoff types is highly sensitive to the accuracy of the estimated soil properties. Leveraging publicly available data sets and reproducible workflows, our approach offers greater understanding of predominant runoff generating processes over a continental extent and expands the technical resources available to environmental planners, regulators, and modellers. [ABSTRACT FROM AUTHOR]

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. • We measure the strength of heat-carbon feedback by causal inference. • Soil respiration dominates the carbon exchange in the built environment. • Urban irrigation induces a trade-off of heat and carbon mitigation in west U.S. • Urban irrigation effects a strong non-local effect on heat-carbon coupling in west U.S. [ABSTRACT FROM AUTHOR]

Surface bare soil albedo is an important variable in climate modeling studies and satellite-based retrievals of land-surface properties. In this study, we used multiyear 500 m albedo products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive the bare soil albedo for seven spectral bands and three broadbands over the contiguous United States (CONUS). The soil line based on red and green spectral signatures derived from MODIS data was used as the basis to detect and extract bare soil albedo. A comparison against bare soil albedo derived from 30 m Landsat data has been made, showing that the MODIS bare soil albedo had a bias of 0.003 and a root-mean-square-error (RMSE) of 0.036. We found that the bare soil albedo was negatively correlated with soil moisture from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), with a relatively stable exponential relationship reflecting the darkening effect that moisture has on most soils. However, quantification of the relationship between bare soil albedo and soil moisture still needs to be improved through simultaneous and instantaneous measurements at a finer spatial resolution. Statistics of the multiyear climatological bare soil albedos calculated using soil types and the International Geosphere-Biosphere Programme (IGBP) land cover types suggest that: Land cover type is a better indicator for determining the magnitude of bare soil albedos for the vegetated areas, as the vegetation density is correlated with soil moisture; and soil type is a better indicator for determining the slope of soil lines over sparsely vegetated areas, as it contains information of the soil texture, roughness, and composition. The generated bare soil albedo can be applied to improve the parameterization of surface energy budget in climate and remote sensing models as well as the retrieval accuracy of some satellite products.

Wetlands provide key functions in the landscape from improving water quality, to regulating flows, to providing wildlife habitat. Over half of the wetlands in the contiguous United States (CONUS) have been converted to agricultural and urban land uses. However, over the last several decades, research has shown the benefits of wetlands to hydrologic, chemical, biological processes, spurring the creation of government programs and private initiatives to restore wetlands. Initiatives tend to focus on individual wetland creation, yet the greatest benefits are achieved when strategic restoration planning occurs across a watershed or multiple watersheds. For watershed-level wetland restoration planning to occur, informative data layers on potential wetland areas are needed. We created an indicator of potential wetland areas (PWA), using nationally available datasets to identify characteristics that could support wetland ecosystems, including: poorly drained soils and low-relief landscape positions as indicated by a derived topographic data layer. We compared our PWA with the National Wetlands Inventory (NWI) from 11 states throughout the CONUS to evaluate their alignment. The state-level percentage of NWI-designated wetlands directly overlapping the PWA ranged from 39 to 95%. When we included NWI that was immediately adjacent to the overlapping NWI, our range of correspondence to NWI ranged from 60 to 99%. Wetland restoration is more likely on certain landscapes (e.g., agriculture) than others due to the lack of substantive infrastructure and the potential for the restoration of hydrology; therefore, we combined the National Land Cover Dataset (NLCD) with the PWA to identify potentially restorable wetlands on agricultural land (PRW-Ag). The PRW-Ag identified a total of over 46 million ha with the potential to support wetlands. The largest concentrations of PRW-Ag occurred in the glaciated corn belt of the upper Mississippi River from Ohio to the Dakotas and in the Mississippi Alluvial Valley. The PRW-Ag layer could assist land managers in identifying sites that may qualify for enrollment in conservation programs, where planners can coordinate restoration efforts, or where decision makers can target resources to optimize the services provided across a watershed or multiple watersheds. [ABSTRACT FROM AUTHOR]

The contiguous US exhibits a wide variety of precipitation regimes, first, because of the wide range of latitudes and altitudes. The physiographic units with a basic meridional configuration contribute to the differentiation between east and west in the country while generating some large interior continental spaces. The frequency distribution of daily precipitation amounts almost anywhere conforms to a negative exponential distribution, reflecting the fact that there are many small daily totals and few large ones. Positive exponential curves, which plot the cumulative percentages of days with precipitation against the cumulative percentage of the rainfall amounts that they contribute, can be evaluated through the Concentration Index. The Concentration Index has been applied to the contiguous United States using a gridded climate dataset of daily precipitation data, at a resolution of 0.25°, provided by CPC/NOAA/OAR/Earth System Research Laboratory, for the period between 1956 and 2006. At the same time, other rainfall indices and variables such as the annual coefficient of variation, seasonal rainfall regimes and the probabilities of a day with precipitation have been presented with a view to explaining spatial CI patterns. The spatial distribution of the CI in the contiguous United States is geographically consistent, reflecting the principal physiographic and climatic units of the country. Likewise, linear correlations have been established between the CI and geographical factors such as latitude, longitude and altitude. In the latter case the Pearson correlation coefficient (r) between this factor and the CI is −0.51 ( p -value < 0.001). For annual probability of days with precipitation and the CI there is also a significant and negative correlation, r = −0.25 ( p -value < 0.001). [ABSTRACT FROM AUTHOR]

Conservation of the wolverine (Gulo gulo) at the southern extent of its North American range requires reliable understandings of past and present distribution patterns and broad-scale habitat relations. We compiled 820 verifiable and documented records of wolverine occurrence (specimens, DNA detections, photos, and accounts of wolverines being killed or captured) in the contiguous United States from museums, the literature, and institutional archives. We spatially referenced 729 records with areal precision ≤1 township (93.2 km²) and temporal precision ≤10 years. Historical records (1827-1960) were located primarily in the western mountains and Great Lakes region. However, our data suggest that the historical distribution of wolverines in the Cascade Range and Sierra Nevada was disjunct, contradicting previous interpretations. Our results indicate that wolverine range in the contiguous United States had contracted substantially by the mid-1900s. Current records (1995-2005) are limited to north-central Washington, northern and central Idaho, western Montana, and northwestern Wyoming. We investigated potential relations between wolverines and alpine vegetation, cold temperatures, and spring snow cover by comparing the distribution of historical wolverine records with Kuchler's potential natural vegetation types, Holdridge's climatic life zones, and EASE snow-cover maps during the latter portion of the wolverine denning period (15 Apr-14 May). In the western mountains, historical wolverine records generally occurred in or near alpine vegetation and climatic conditions, especially at the limits of their distribution in the Cascade Range, Sierra Nevada, and southern Rocky Mountains. However, the only habitat layer that fully accounted for historical distribution patterns was spring snow cover. Causal factors for the extirpation of wolverines from the southern portions of their range in the contiguous United States are unknown, but are likely related to high levels of human-caused mortality and low to nonexistent immigration rates. [ABSTRACT FROM AUTHOR]

Urban climate research has surged in the past decades due to its unique position in addressing the concomitant issues of the rapid global urbanization and emergent climate changes. Nevertheless, there is still a lack of holistic and system-based toolkit for investigating complex dynamics of urban climate systems, especially those of hydroclimate extremes. Here we propose a novel framework based on complex networks for analyzing the topological structure of the urban climate in the contiguous United States (CONUS). In addition, we simulate CONUS urban networks using Kuramoto model and the master stability function formalism, which sheds new light on the occurrence and evolution of heat waves as dynamic synchronization processes. It is found that the CONUS urban climate networks are highly modular with spatial hierarchical (hub-periphery) organization. The small-world effect is also manifest in these networks with the characteristic path length between any pair of nodes no more than 5. For globally connected urban networks, as the sparsity of connections increases (with threshold of correlation coefficient), the path length decreases roughly linearly, whereas the synchronizability reduces exponentially. • We carried out network analysis for structure-dynamic interactions in urban climate system. • CONUS urban climate networks exhibit high modularity and small-worldness. • Heat extremes are simulated as synchronization processes in urban climate networks. • The intensity of heatwaves can be represented by the order parameter of the synchronized state. [ABSTRACT FROM AUTHOR]

Surface bare soil albedo is an important variable in climate modeling studies and satellite-based retrievals of land-surface properties. In this study, we used multiyear 500 m albedo products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive the bare soil albedo for seven spectral bands and three broadbands over the contiguous United States (CONUS). The soil line based on red and green spectral signatures derived from MODIS data was used as the basis to detect and extract bare soil albedo. A comparison against bare soil albedo derived from 30 m Landsat data has been made, showing that the MODIS bare soil albedo had a bias of 0.003 and a root-mean-square-error (RMSE) of 0.036. We found that the bare soil albedo was negatively correlated with soil moisture from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), with a relatively stable exponential relationship reflecting the darkening effect that moisture has on most soils. However, quantification of the relationship between bare soil albedo and soil moisture still needs to be improved through simultaneous and instantaneous measurements at a finer spatial resolution. Statistics of the multiyear climatological bare soil albedos calculated using soil types and the International Geosphere-Biosphere Programme (IGBP) land cover types suggest that: Land cover type is a better indicator for determining the magnitude of bare soil albedos for the vegetated areas, as the vegetation density is correlated with soil moisture; and soil type is a better indicator for determining the slope of soil lines over sparsely vegetated areas, as it contains information of the soil texture, roughness, and composition. The generated bare soil albedo can be applied to improve the parameterization of surface energy budget in climate and remote sensing models as well as the retrieval accuracy of some satellite products. [ABSTRACT FROM AUTHOR]

Recent studies have explored the relationship between aerosol optical depth (AOD) measurements by satellite sensors and concentrations of particulate matter with aerodynamic diameters less than 2.5 μm (PM 2.5 ). However, relatively little is known about spatial and temporal patterns in this relationship across the contiguous United States. In this study, we investigated the relationship between US Environmental Protection Agency estimates of PM 2.5 concentrations and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements provided by two NASA satellites (Terra and Aqua) across the contiguous United States during 2005. We found that the combined use of both satellite sensors provided more AOD coverage than the use of either satellite sensor alone, that the correlation between AOD measurements and PM 2.5 concentrations varied substantially by geographic location, and that this correlation was stronger in the summer and fall than that in the winter and spring.