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In the face of future climate change, it is prudent to seek sustainable adaptation strategies to address regional and local impacts. These impacts are multidimensional, involving interdependencies between systems (weather, urban land use, agriculture, etc.) that are typically modeled independently. To achieve a holistic understanding and thus identify more effective strategies for addressing and/or mitigating impacts, an integrated interdisciplinary research approach is essential. Here we discuss the broader challenges and threats faced by regions encompassing large bodies of water. We illustrate with North America's Great Lakes region, discussing how an integrated model of atmosphere, land, and lake could provide critical information to inform decisions. We stress the need to include input from diverse stakeholders in the development of tools to ensure the quality and usability of impact assessments. Research investments toward such capabilities should engage multiple disciplines including atmospheric sciences, hydrodynamics, hydrology, and biogeochemistry as well as data analytics and modeling. Also, detailed measurement and documentation of urban and agricultural land use, lake surface temperature and ice‐cover, and observations of energy and mass exchanges at the interfaces of atmosphere, land, and water are needed. We envision the development of an integrated set of modeling tools that will improve both the utility of weather forecasts and long‐term climate projections of the impacts on ecosystem sustainability, hydrometeorological extremes, engineering design, human health, and socioeconomic systems. Such a modeling system can serve as a template for other regions with cities, large lakes, inland seas, and coastlines facing similar kinds of climate change impacts. Key Points: Land‐lake‐atmosphere interactions impact human and natural systems in regions such as the North American Great Lakes, but the uncertainty in measurement, observations, and modeling remains largeLand, water, and atmosphere are typically modeled independently rather than as the complex, interconnect, multiscale systems that they representThere is a need for a collaborative framework for stakeholders to design interdisciplinary and coupled tools for forecasting and evaluating the impacts of climate change and related policy and interventions [ABSTRACT FROM AUTHOR]

We present the measurement of cloud base height (CBH) derived from the Doppler Lidar (DL), Ceilometer (CM) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite over a high altitude station in the central Himalayan region for the first time. We analyzed six cases of cloud overpass during the daytime convection period by using the cloud images captured by total sky imager. The occurrence of thick clouds (> 50%) over the site is more frequent than thin clouds (< 40%). In every case, the CBH indicates less than 1.2 km, above ground level (AGL) observed by both DL and CM instruments. The presence of low level clouds in the height-time variation of signal to noise ratio of DL and backscatter of CM shows a similar diurnal pattern on all days. Cloud fraction is found to be maximum during the convective period. The CBH estimated by the DL and CM showed reasonably good correlation (R² = 0.76). The DL observed updraft fraction and cloud base vertical velocity also shows good correlation (R² = 0.66). The inter-comparison between DL and CM will have implications in filling the gap of CBH measurements by the DL, in absence of CM. More deployments of such instruments will be invaluable for the validations of meteorological models over the observationally sparse Indian regions. [ABSTRACT FROM AUTHOR]

Aerosol radiative effects and thermodynamic responses over South Asia are examined with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for March 2012. Model results of aerosol optical depths (AODs) and extinction profiles are analyzed and compared to satellite retrievals and two ground-based lidars located in northern India. The WRF-Chem model is found to heavily underestimate the AOD during the simulated premonsoon month and about 83% of the model's low bias is due to aerosol extinctions below ∼2 km. Doubling the calculated aerosol extinctions below 850 hPa generates much better agreement with the observed AOD and extinction profiles averaged over South Asia. To separate the effect of absorption and scattering properties, two runs were conducted: in one run (Case I), the calculated scattering and absorption coefficients were increased proportionally, while in the second run (Case II) only the calculated aerosol scattering coefficient was increased. With the same AOD and extinction profiles, the two runs produce significantly different radiative effects over land and oceans. On the regional mean basis, Case I generates 48% more heating in the atmosphere and 21% more dimming at the surface than Case II. Case I also produces stronger cooling responses over the land from the longwave radiation adjustment and boundary layer mixing. These rapid adjustments offset the stronger radiative heating in Case I and lead to an overall lower-troposphere cooling up to -0.7Kday-1, which is smaller than that in Case II. Over the ocean, direct radiative effects dominate the heating rate changes in the lower atmosphere lacking such surface and lower atmosphere adjustments due to fixed sea surface temperature, and the strongest atmospheric warming is obtained in Case I. Consequently, atmospheric dynamics (boundary layer heights and meridional circulation) and thermodynamic processes (water vapor and cloudiness) are shown to respond differently between Case I and Case II, underlining the importance of determining the exact portion of scattering or absorbing aerosols that lead to the underestimation of aerosol optical depth in the model. In addition, the model results suggest that both the direct radiative effect and rapid thermodynamic responses need to be quantified for understanding aerosol radiative impacts. [ABSTRACT FROM AUTHOR]

Aerosol radiative effects and thermodynamic responses over South Asia are examined with a version of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for March 2012. Model results of Aerosol Optical Depth (AOD) and extinction profiles are analyzed and compared to satellite retrievals and two ground-based lidars located in the northern India. The WRF-Chem model is found to underestimate the AOD during the simulated pre-monsoon month and about 83 % of the model low-bias is due to aerosol extinctions below ~2 km. Doubling the calculated aerosol extinctions below 850 hPa generates much better agreement with the observed AOD and extinction profiles averaged over South Asia. To separate the effect of absorption and scattering properties, two runs were conducted: in one run (Case I), the calculated scattering and absorption coefficients were increased proportionally, while in the second run (Case II) only the calculated aerosol scattering coefficient was increased. With the same AOD and extinction profiles, the two runs produce significantly different radiative effects over land and oceans. On the regional mean basis, Case I generates 48 % more heating in the atmosphere and 21 % more dimming at the surface than Case II. Case I also produces stronger cooling responses over the land from the longwave radiation adjustment and boundary layer mixing. These rapid adjustments offset the stronger radiative heating in Case I and lead to an overall lower-troposphere cooling up to -0.7 K day-1, which is smaller than that in Case II. Over the ocean, direct radiative effects dominate the heating rate changes in the lower atmosphere lacking such surface and lower atmosphere adjustments due to fixed sea surface temperature, and the strongest atmospheric warming is obtained in Case I. Consequently, atmospheric dynamics (boundary layer heights and meridional circulation) and thermodynamic processes (water vapor and cloudiness) are shown to respond differently between Case I and Case II underlying the importance of determining the exact portion of scattering or absorbing aerosols that lead to the underestimation of aerosol optical depth in the model. In addition, the model results suggest that both direct radiative effect and rapid thermodynamic responses need to be quantified for understanding aerosol radiative impacts. [ABSTRACT FROM AUTHOR]

Cultivation of the terrestrial land surface can create either a source or sink of atmospheric CO2, depending on land management practices. The Community Land Model (CLM) provides a useful tool for exploring how land use and management impact the soil carbon pool at regional to global scales. CLM was recently updated to include representation of managed lands growing maize, soybean, and spring wheat. In this study, CLM-Crop is used to investigate the impacts of various management practices, including fertilizer use and differential rates of crop residue removal, on the soil organic carbon (SOC) storage of croplands in the continental United States over approximately a 170-year period. Results indicate that total US SOC stocks have already lost over 8 Pg C (10 %) due to land cultivation practices (e.g., fertilizer application, cultivar choice, and residue removal), compared to a land surface composed of native vegetation (i.e., grasslands). After long periods of cultivation, individual subgrids (the equivalent of a field plot) growing maize and soybean lost up to 65% of the carbon stored compared to a grassland site. Crop residue management showed the greatest effect on soil carbon storage, with low and medium residue returns resulting in additional losses of 5 and 3.5 %, respectively, in US carbon storage, while plots with high residue returns stored 2% more carbon. Nitrogenous fertilizer can alter the amount of soil carbon stocks significantly. Under current levels of crop residue return, not applying fertilizer resulted in a 5% loss of soil carbon. Our simulations indicate that disturbance through cultivation will always result in a loss of soil carbon, and management practices will have a large influence on the magnitude of SOC loss. [ABSTRACT FROM AUTHOR]

Cultivation of the terrestrial land surface can create either a source or sink of atmospheric CO2, depending on land management practices. The Community Land Model (CLM) provides a useful tool to explore how land use and management impact the soil carbon pool at regional to global scales. CLM was recently updated to include representation of managed lands growing maize, soybean, and spring wheat. In this study, CLM-Crop is used to investigate the impacts of various management practices, including fertilizer use and differential rates of crop residue removal, on the soil organic carbon (SOC) storage of croplands in the continental United States over approximately a 170 year period. Results indicate that total US SOC stocks have already lost over 8PgC (10%) due to land cultivation practices (e.g., fertilizer application, cultivar choice, and residue removal), compared to a land surface composed of native vegetation (i.e., grasslands). After long periods of cultivation, individual plots growing maize and soybean lost up to 65 % of the carbon stored, compared to a grassland site. Crop residue management showed the greatest effect on soil carbon storage, with low and medium residue returns resulting in additional losses of 5% and 3.5%, respectively, in US carbon storage, while plots with high residue returns stored 2% more carbon. Nitrogenous fertilizer can alter the amount of soil carbon stocks significantly. Under current levels of crop residue return, not applying fertilizer resulted in a 5% loss of soil carbon. Our simulations indicate that disturbance through cultivation will always result in a loss of soil carbon, and management practices will have a large influence on the magnitude of SOC loss. [ABSTRACT FROM AUTHOR]

The potential impact of climate change on agriculture is uncertain. In addition, agriculture could influence above-and below-ground carbon storage. Development of models that represent agriculture is necessary to address these impacts. We have developed an approach to integrate agriculture representations for three crop types - maize, soybean, and spring wheat - into the coupled carbon-nitrogen version of the Community Land Model (CLM), to help address these questions. Here we present the new model, CLMCrop, validated against observations from two AmeriFlux sites in the United States, planted with maize and soybean. Seasonal carbon fluxes compared well with field measurements for soybean, but not as well for maize. CLM-Crop yields were comparable with observations in countries such as the United States, Argentina, and China, although the generality of the crop model and its lack of technology and irrigation made direct comparison difficult. CLM-Crop was compared against the standard CLM3.5, which simulates crops as grass. The comparison showed improvement in gross primary productivity in regions where crops are the dominant vegetation cover. Crop yields and productivity were negatively correlated with temperature and positively correlated with precipitation, in agreement with other modeling studies. In case studies with the new crop model looking at impacts of residue management and planting date on crop yield, we found that increased residue returned to the litter pool increased crop yield, while reduced residue returns resulted in yield decreases. Using climate controls to signal planting date caused different responses in different crops. Maize and soybean had opposite reactions: when low temperature threshold resulted in early planting, maize responded with a loss of yield, but soybean yields increased. Our improvements in CLM demonstrate a new capability in the model - simulating agriculture in a realistic way, complete with fertilizer and residue management practices. Results are encouraging, with improved representation of human influences on the land surface and the potentially resulting climate impacts. [ABSTRACT FROM AUTHOR]

Several recent observational studies have shown organic carbon aerosols to be a significant source of absorption of solar radiation. The absorbing part of organic aerosols is referred to as brown carbon. Comparisons with observations indicate that model-simulated aerosol absorption is under-estimated in global models, one of the reasons being the neglect of brown carbon. Using a global chemical transport model coupled with a radiative transfer model, we estimate for the first time the enhanced absorption of solar radiation due to "brown" carbon (BrC) in a global model. When BrC is included, the simulated wavelength dependence of aerosol absorption, as measured by the Angstrom exponent increases from 0.9 to 1.2 and thus agrees better with AERONET spectral observations at 440-870 nm. The resulting absorbing aerosol optical depth increases by 3-18 % at 550 nm and up to 56 % at 350 nm. The global simulations suggest that BrC contributes up to +0.25 Wm-2 or 19% of the absorption by anthropogenic aerosols, of which 72% is attributed to black carbon, and 9% is due to sulfate and non-absorbing organic aerosols coated on black carbon. Like black carbon, the overall forcing of BrC at the top of the atmosphere (TOA) is a warming effect (+0.11 Wm-2), while the effect at the surface is a reduction or dimming (-0.14 Wm-2). Because of the inclusion of BrC in our model, the direct radiative effect of organic carbonaceous aerosols changes from cooling (-0.08 Wm-2) to warming (+0.025 Wm-2) at the TOA, on a global mean basis. Over source regions and above clouds, the absorption of BrC is more significant and thus can play an important role in photochemistry and the hydrologic cycle. [ABSTRACT FROM AUTHOR]

The article presents information on a study conducted on the impact of climate change on agriculture. In the study, Community Land Model (CLM)-Crop was validated against observations from two agricultural sites in the U.S., that were planted with maize and soybean. CLM-Crop was compared with the standard CLM3.5, which is associated with crops as grass. The study found that crop yields and productivity had a negative correlation with temperature, and a positive correlation with precipitation.

The radiosonde humidity profiles available during the Ganges Valley Experiment were compared to those simulated from the regional Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem) and the global reanalysis datasets. Large biases were revealed. On a monthly mean basis at Nainital, located in northern India, the WRF-Chem model simulates a large moist bias in the free troposphere (up to +20%) as well as a large dry bias in the boundary layer (up to -30%). While the overall pattern of the biases is similar, the magnitude of the biases varies from time to time and from one location to another. At Thiruvananthapuram, the magnitude of the dry bias is smaller, and in contrast to Nainital, the higher-resolution regional WRF-Chem model generates larger moist biases in the upper troposphere than the global reanalysis data. Furthermore, the humidity biases in the upper troposphere, while significant, have little impact on the model estimation of column aerosol optical depth (AOD). The frequent occurrences of the dry boundary-layer bias simulated by the large-scale models tend to lead to the underestimation of AOD. It is thus important to quantify the humidity vertical profiles for aerosol simulations over South Asia. [ABSTRACT FROM AUTHOR]

An introduction to a series of articles about the regional aerosol warming experiment - Ganges Valley aerosol experiment (RAWEX-GVAX) published within the issue is presented.