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

High‐resolution modeling became popular in recent years due to the availability of multisource observations, advances in understanding fine‐scale processes, and improvements in computing facilities. However, modeling of hydrological changes over mountainous regions is still a great challenge due to the sensitivity of highland water cycle to global warming, tightly coupled hydrothermal processes, and limited observations. Here we show a successful high‐resolution (3 km) land surface modeling over the Sanjiangyuan region located in the eastern Tibetan plateau, which is the headwater of three major Asian rivers. By developing a new version of a Conjunctive Surface‐Subsurface Process model named as CSSPv2, we increased Nash‐Sutcliffe efficiency by 62–130% for streamflow simulations due to the introduction of a storage‐based runoff generation scheme, reduced errors by up to 31% for soil moisture modeling after considering the effect of soil organic matter on porosity and hydraulic conductivity. Compared with ERA‐Interim and Global Land Data Assimilation System version 1.0 reanalysis products, CSSPv2 reduced errors by up to 30%, 69%, 92%, and 40% for soil moisture, soil temperature, evapotranspiration, and terrestrial water storage change, respectively, as evaluated against in situ and satellite observations. Moreover, CSSPv2 well captured the elevation‐dependent ground temperature warming trends and the decreased frozen dates during 1979–2014, and significant increasing trends (p < 0.05) in evapotranspiration and terrestrial water storage during 1982–2011 and 2003–2014 respectively, while ERA‐Interim and Global Land Data Assimilation System version 1.0 showed no trends or even negative trends. This study implies the necessity of developing high‐resolution land surface models in realistically representing hydrological changes over highland areas that are sentinels to climate change. Key Points: CSSPv2 land model was developed over headwaters with conjunctive surface‐subsurface flows, storage‐based runoff, and soil organic matterCSSPv2 outperformed global reanalysis for simulations of hydrological cycle as validated with multisource observationsCSSPv2 captured elevation‐dependent warming, increasing ET and TWS in the satellite era, while global reanalysis failed over headwaters [ABSTRACT FROM AUTHOR]

This study investigates the future U.S. PM 2.5 pollution under multiple emissions scenarios, climate states, and long-range transport (LRT) effects using the regional Community Multi-scale Air Quality (CMAQ) model integrated with a regional climate model. CMAQ with fixed chemical lateral boundary conditions (LBCs) successfully reproduces the present-day PM 2.5 pollution and its major species in rural and suburban areas, but has some discrepancies in urban areas such as the Los Angeles Basin, where detailed emissions and meteorology conditions cannot be resolved by the 30 km grid. Its performance is slightly worsened when using dynamic chemical LBCs from global chemical transport model (CTM) simulations, which provide cleaner conditions into the CMAQ lateral boundaries. Under future Intergovernmental Panel on Climate Change (IPCC) emission scenarios, CMAQ projects large PM 2.5 reductions (∼40% for A1B and ∼20% for A1Fi scenario) in the eastern United States, but slight to moderate increases (∼5% for A1B and ∼10% for A1Fi) in the western United States. The projected increases are particularly large (up to 30%) near the Mexico-U.S. border, suggesting that Mexico is a major source for future U.S. PM 2.5 pollution. The effect from climate change alone is estimated to increase PM 2.5 levels ubiquitously (∼5% for both A1B and A1Fi) over the United States, except for a small decrease in the Houston, Texas area, where anthropogenic non-methane volatile organic compounds (NMVOCs) emissions dominate. This climate penalty, however, is substantially smaller than effects of emissions change, especially in the eastern United States. Future PM 2.5 pollution is affected substantially (up to −20%) by changes in SO 2 emissions and moderately (3–5%) by changes in NO x and NH 3 emissions. The long-range transport (LRT) effects, which are estimated by comparing CMAQ simulations with fixed and dynamic LBCs, are regional dependent, causing up to 10–20% decrease over the western United States in future summertime PM 2.5 pollution. Therefore, it is important to consider the relative contributions of emissions scenarios, climate conditions, and LRT to the major PM 2.5 components in future U.S. air quality regulation. [ABSTRACT FROM AUTHOR]

This study examines the effects of five cumulus closure assumptions on simulations of summer precipitation in the continental U.S. by utilizing an ensemble cumulus parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation. Results demonstrate that closure algorithms significantly affect the summer mean, daily frequency and intensity, and diurnal variation of precipitation, with strong regional dependence. Overall, the vertical velocity (W) closure produces the smallest summer mean biases, while the moisture convergence (MC) closure most realistically reproduces daily variability. Both closures have advantages over others in simulating U.S. daily rainfall frequency distribution, though both slightly overestimate intense rain events. The MC closure is superior at capturing summer rainfall amount, daily variability, and heavy rainfall frequency over the Central U.S., but systematically produces wet biases over the North American Monsoon (NAM) region and Southeast U.S., which can be reduced by using the W closure. The instability tendency (TD) and the total instability adjustment (KF) closures are better at capturing observed diurnal signals over the Central U.S. and the NAM, respectively. The results reasonably explain the systematic behaviors of several major cumulus parameterizations. A preliminary experiment combining two optimal closures (averaged moisture convergence and vertical velocity) in the ECP scheme significantly reduced the wet (dry) biases over the Southeast U.S. in the summer of 1993 (2003), and greatly improved daily rainfall correlations over the NAM. Further improved model simulation skills may be achieved in the future if optimal closures and their appropriate weights can be derived at different time scales based on specific climate regimes. [ABSTRACT FROM AUTHOR]

Excessive rainfall can cause catastrophic socio-economic losses to a community or nation. An analysis of changes in gross regional product identifies ways in which extreme precipitation affects global economic productivity.Macroeconomic impact of increases in wet days. [ABSTRACT FROM AUTHOR]

A consistent modeling framework with nested global and regional chemical transport models (CTMs) is used to separate and quantitatively assess the relative contributions to projections of future U.S. ozone pollution from the effects of emissions changes, climate change, long-range transport (LRT) of pollutants, and differences in modeling design. After incorporating dynamic lateral boundary conditions (LBCs) from a global CTM, a regional CTM's representation of present-day U.S. ozone pollution is notably improved, especially relative to results from the regional CTM with fixed LBCs or from the global CTM alone. This nested system of global and regional CTMs projects substantial surface ozone trends for the 2050's: 6–10 ppb decreases under the ‘clean’ A1B scenario and ∼15 ppb increases under the ‘dirty’ A1Fi scenario. Among the total trends of future ozone, regional emissions changes dominate, contributing negative 25–60% in A1B and positive 30–45% in A1Fi. Comparatively, climate change contributes positive 10–30%, while LRT effects through changing chemical LBCs account for positive 15–20% in both scenarios, suggesting introducing dynamic LBCs could influence projections of the U.S. future ozone pollution with a magnitude comparable to effects of climate change alone. The contribution to future ozone projections due to differences in modeling design, including model formulations, emissions treatments, and other factors between the global and the nested regional CTMs, is regionally dependent, ranging from negative 20% to positive 25%. It is shown that the model discrepancies for present-day simulations between global and regional CTMs can propagate into future U.S. ozone projections systematically but nonlinearly, especially in California and the Southeast. Therefore in addition to representations of emissions change and climate change, accurate treatment of LBCs for the regional CTM is essential for projecting the future U.S. ozone pollution. [ABSTRACT FROM AUTHOR]

This study comprehensively evaluates the effects of twelve cumulus parameterization (CUP) schemes on simulations of 1993 and 2008 Central US summer floods using the regional climate-weather research and forecasting model. The CUP schemes have distinct skills in predicting the summer mean pattern, daily rainfall frequency and precipitation diurnal cycle. Most CUP schemes largely underestimate the magnitude of Central US floods, but three schemes including the ensemble cumulus parameterization (ECP), the Grell-3 ensemble cumulus parameterization (G3) and Zhang-McFarlane-Liang cumulus parameterization (ZML) show clear advantages over others in reproducing both floods location and amount. In particular, the ECP scheme with the moisture convergence closure over land and cloud-base vertical velocity closure over oceans not only reduces the wet biases in the G3 and ZML schemes along the US coastal oceans, but also accurately reproduces the Central US daily precipitation variation and frequency distribution. The Grell (GR) scheme shows superiority in reproducing the Central US nocturnal rainfall maxima, but others generally fail. This advantage of GR scheme is primarily due to its closure assumption in which the convection is determined by the tendency of large-scale instability. Future study will attempt to incorporate the large-scale tendency assumption as a trigger function in the ECP scheme to improve its prediction of Central US rainfall diurnal cycle. [ABSTRACT FROM AUTHOR]

Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species. [ABSTRACT FROM AUTHOR]

This study proposes an end-to-end forecasting framework to incorporate operational seasonal climate forecasts to help farmers improve their decisions prior to the crop growth season, which are vulnerable to unanticipated drought conditions. The framework couples a crop growth model with a decision-making model for rainfed agriculture and translates probabilistic seasonal forecasts into more user-related information that can be used to support farmers' decisions on crop type and some market choices (e.g., contracts with ethanol refinery). The regional Climate-Weather Research and Forecasting model (CWRF) driven by two operational general circulation models (GCMs) is used to provide the seasonal forecasts of weather parameters. To better assess the developed framework, CWRF is also driven by observational reanalysis data, which theoretically can be considered as the best seasonal forecast. The proposed framework is applied to the Salt Creek watershed in Illinois that experienced an extreme drought event during 2012 crop growth season. The results show that the forecasts cannot capture the 2012 drought condition in Salt Creek and therefore the suggested decisions can make farmers worse off if the suggestions are adopted. Alternatively, the optimal decisions based on reanalysis-based CWRF forecasts, which can capture the 2012 drought conditions, make farmers better off by suggesting 'no-contract' with ethanol refineries. This study suggests that the conventional metric used for ex ante value assessment is not capable of providing meaningful information in the case of extreme drought. Also, it is observed that institutional interventions (e.g., crop insurance) highly influences farmers' decisions and, thereby, the assessment of forecast value. [ABSTRACT FROM AUTHOR]

Climate changes over China from the present (1990-1999) to future (2046-2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility. [ABSTRACT FROM AUTHOR]

Abstract: A global climate chemistry model CAM-Chem is driven by the meteorology output from community climate system model version 3 (CCSM3) to investigate the relative contributions of changes in local anthropogenic emissions (LE) versus changes in remote anthropogenic emissions (RE) to global surface ozone air quality in 2050. On major ozone pollution regions (Europe, the United States, Asia), the effects are examined following three distinct pathways, A1FI, A1B and B1, from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) to address the uncertainty in projections of future climate and emissions. We find that projected changes in anthropogenic emissions under the A1FI scenario lead to an increase of 5–14 ppb in summertime daily maximum 8-h (DM8H) ozone concentration over U.S. by 2050, of which 48% is contributed by LE changes and 52% is contributed by RE changes. For Europe, the change in local emissions contributes 62% of the surface ozone increase in 2050 summer, while 38% of the increase is attributed to remote emission change. For Asia, changes in LE dominate the 2050 surface ozone increase with a magnitude of 10–30 ppb on summertime DM8H ozone concentration. However, under the A1B and B1 scenarios, contributions from LE changes are much larger than that from RE changes over all three regions except Asia under the B1 scenario, in which the RE changes contribute 31% of total change. The results indicate that for the United States and Europe, pollution control is a local issue under global low emission situations, while it becomes an international issue when fossil fuel use is rapidly increasing. Due to the weak Euro-Asia transport, local emission increase seems to be the main force for Asia''s ozone air quality change under all cases except the low emission scenario B1. Therefore, the strategies for regional air quality control need to be based on global emission situation. [Copyright &y& Elsevier]

Abstract: We investigate the effects of projected global changes in climate and human-related emissions for the year 2050 relative to 2000 for trends in the potential risk of hazardous ozone pollution episodes using a global climate chemistry model, CAM-Chem, driven by meteorology output from Community Climate System Model 3. Three distinct pathways from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios A1FI, A1B and B1 are considered to address the range and uncertainty in projected climate and emission changes. The projected changes in extreme climate conditions are likely to intensify the associated extreme weather conditions that foster the risk of high ozone pollution episodes over many parts of the world. It is found that the changes in regional high surface ozone concentration by 2050 largely depends on changes in the anthropogenic emissions of ozone precursors. Our analysis under projected climate and emissions on the frequency of “hazardous ozone days” in which the peak ozone concentration exceed the limit in the summer of 2050, based on 8 and 1-h standards, show that the risk of hazardous ozone pollution episodes will likely increase in developing regions, but changes of risk in developed regions depend on scenarios. For three major pollutant regions, north America, Europe and East Asia under 8-h definition, the hazardous risk in all regions increases under the A1FI scenario with a potential rate of 39–79 days/summer by 2050, but it is likely to decrease over North America and Europe under the A1B and B1 scenarios. The risk on Europe under the A1B and B1 scenarios can be ignored, but a significant rate of 6–71 days/summer still occur on other regions. The relative variability in projected frequency of hazardous ozone days increase by using the 1-h definition, although it shows the highest risk of 17–59 days/summer under the A1FI scenario. The higher variability can be understood through statistical analysis of cumulative frequency distribution of daily peak ozone concentrations, which shows that the changes of surface ozone level are more significantly presented on the high value part. [Copyright &y& Elsevier]

• The impacts of monsoon rainfall on food insecurity differed by earthquake exposure. • Low rainfall was linked with greater food insecurity in non-impacted areas. • More rainfall was positively linked with food insecurity in affected areas. • This is likely due to rainfall increasing landslides in earthquake-affected regions. Food insecurity is a key global health challenge that is likely to be exacerbated by climate change. Though climate change is associated with an increased frequency of extreme weather events, little is known about how multiple environmental shocks in close succession interact to impact household health and well-being. In this paper, we assess how earthquake exposure followed by monsoon rainfall anomalies affect food insecurity in Nepal. We link food security data from the 2016 Nepal Demographic and Health Survey to data on shaking intensity during the 2015 Gorkha earthquake and rainfall anomalies during the 2015 monsoon season. We then exploit spatial variation in exposure to the earthquake and monsoon rainfall anomalies to isolate their independent and compound effects. We find that earthquake exposure alone was not associated with an increased likelihood of food insecurity, likely due in part to effective food aid distribution. However, the effects of rainfall anomalies differed by severity of earthquake exposure. Among households minimally impacted by the earthquake, low rainfall was associated with increased food insecurity, likely due to lower agricultural productivity in drought conditions. Among households that experienced at least moderate shaking, greater rainfall was positively associated with food insecurity, particularly in steep, mountainous areas. In these locations, rainfall events disproportionately increased landslides, which damaged roads, disrupted distribution of food aid, and destroyed agricultural land and assets. Additional research on the social impacts of compound environmental shocks is needed to inform adaptation strategies that work to improve well-being in the face of climate change. [ABSTRACT FROM AUTHOR]