As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers' needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and "usability" of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatialscale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

Abstract.Regional climate change impact (CCI) studies have widely involved downscaling and bias correcting (BC) global climate model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables (evapotranspiration (ET), runoff, snow water equivalent (SWE), and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ-Andrews). Simulation results from the coupled ECHAM5-MPI-OM model with A1B emission scenario were first dynamically downscaled to 12 km resolution with the WRF model. Then a quantile-mapping-based statistical downscaling model was used to downscale them into 1/16 • resolution daily climate data over historical and future periods. Two climate data series were generated, with bias correction (BC) and without bias correction (NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological data sets. These impact models include a macroscale hydrologic model (VIC), a coupled cropping system model (VICCropSyst), an ecohydrological model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS).Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ-Andrews's ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at regional scales.We conclude that there are trade-offs between using BC climate data for offline CCI studies versus directly modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; for example, BC may be more important when considering impacts on reservoir operations in mountainPublished by Copernicus Publications on behalf of the European Geosciences Union. M. Liu et al.: What is the importance of climate mod...

Abstract. High-resolution simulations were performed to assess the impact of different parameterization schemes, surface initialization datasets, and analysis nudging on lower-tropospheric conditions near Lake Michigan. Simulations were run where climatological or coarse-resolution surface initialization datasets were replaced by high-resolution, real-time datasets depicting lake surface temperatures (SST), green vegetation fraction (GVF), and soil moisture and temperature (SOIL). Comparison of a baseline simulation employing a configuration similar to that used at the Environmental Protection Agency (“EPA”) to another simulation employing an alternative set of parameterization schemes (referred to as “YNT”) showed that the EPA configuration produced more accurate analyses on the outermost 12-km resolution domain, but that the YNT configuration was superior for higher-resolution nests. The diurnal evolution of the surface energy fluxes was similar in both simulations on the 12-km grid but differed greatly on the 1.3-km grid where the EPA simulation had much smaller sensible heat flux during the daytime and physically unrealistic ground heat flux. Switching to the YNT configuration led to substantial decreases in root mean square error for 2-m temperature and 2-m water vapor mixing ratio on the 1.3-km grid. Additional improvements occurred when the high-resolution satellite-derived surface datasets were incorporated into the modeling platform, with the SOIL dataset having the largest positive impact on temperature and water vapor. The GVF and SST datasets also produced more accurate temperature and water vapor analyses, but degradations in wind speed, especially when using the GVF dataset. The most accurate simulations were obtained when using the high-resolution SST and SOIL datasets and analysis nudging above 2 km AGL.

Abstract. Surface-level ozone (O3) is a secondary air pollutant that has adverse effects on human health. In the troposphere, O3 is produced in complex cycles of photochemical reactions involving nitrogen oxides (NOx) and volatile organic compounds (VOCs). Determining if O3 levels will be decreased by lowering NOx emissions (“NOx-sensitive”), VOC emissions (“VOC-sensitive”), or both (“the transition zone”) can be done by using the formaldehyde (HCHO; a VOC species) to nitrogen dioxide (NO2; a component of NOx) concentration ratio (HCHO/NO2; “FNR”). Generally, lower FNR values indicate VOC sensitivity, while higher values indicate NOx sensitivity. Despite being a highly populated region with coastal O3 air quality issues, the Lake Michigan region in the United States, including the Chicago, Illinois, metropolitan area (CMA), remains relatively understudied, especially from the satellite perspective. In this work, we present the first study that utilizes TROPOspheric Monitoring Instrument (TROPOMI) satellite data over the Lake Michigan region from 2019–2021 to assess changes in O3 precursor levels and the inferred O3 chemistry sensitivity between (1) O3 season days and CMA O3 exceedance days and (2) weekdays and weekends. Higher NO2 vertical column densities (VCDs), HCHO VCDs, and FNR values are seen throughout the study domain on exceedance days, indicating generally more NOx-sensitive O3 chemistry. The largest change occurs in the areal extent of the transition zone, which decreases by 40 % during exceedance days. Major urban cores in the domain (e.g., Chicago, Illinois; Gary, Indiana; and Milwaukee, Wisconsin) remain VOC-sensitive on exceedance days as the higher NO2 VCDs in these areas counterbalance the regionally higher HCHO VCDs. Utilizing 10 m wind analysis data, we show that the lake breeze circulation is stronger on exceedance days. The strengthening of the lake breeze causes stronger convergence of the wind field along the southwestern Lake Michigan coastline, which can concentrate NO2 emissions originating in this area. This finding provides a possible explanation for the higher TROPOMI NO2 VCDs over the urban core of Chicago on exceedance days. Investigation of 2 m air temperature analysis data reveals that temperatures are higher on exceedance days, which explains the stronger lake breeze circulation and provides a possible cause for the higher TROPOMI HCHO VCDs over the entire region (due to increased temperature-dependent biogenic VOC emissions). Comparing weekdays and weekends, higher FNR values throughout much of the region indicate increasingly NOx-sensitive O3 chemistry on weekends. These changes are driven by lower NO2 VCDs in urban areas, particularly in Chicago, and higher HCHO VCDs in the southern part of the domain on weekends. Overall, our analyses suggest that VOC emissions controls in major urban areas and NOx emissions controls throughout the entire domain are necessary to decrease O3 levels in the Lake Michigan region.

Abstract. Regional climate change impact (CCI) studies have widely involved downscaling and bias-correcting (BC) Global Climate Model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables (evapotranspiration, ET; runoff; snow water equivalent, SWE; and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) Region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ Andrews). Simulation results from the coupled ECHAM5/MPI-OM model with A1B emission scenario were firstly dynamically downscaled to 12 km resolutions with WRF model. Then a quantile mapping based statistical downscaling model was used to downscale them into 1/16th degree resolution daily climate data over historical and future periods. Two series climate data were generated according to the option of bias-correction (i.e. with bias-correction (BC) and without bias-correction, NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological datasets. These impact models include a macro-scale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrologic model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS). Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ Andrews's ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at region scales. We conclude that there are trade-offs between using BC climate data for offline CCI studies vs. direct modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; e.g., BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality (where VOCs are a primary indicator).