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Biomass burning aerosol radiative effects in the Southeast Atlantic depend strongly on meteorological forcing method

Biomass burning aerosols (BBAs) from African fires may strongly impact Earth’s radiation budget in the southeast Atlantic (SEA), but the sign and magnitude of the overall radiative effect (RE) remain uncertain. Aerosol–climate models are needed to separately quantify direct, indirect, and semi-direct REs. Here, we evaluate improved simulations with the UK Met Office's Unified Model and explore REs resulting from various methods used to match observed meteorology (nudging or running forecasts reinitialized at different frequencies). REs are calculated as differences in radiative fluxes between simulations with and without smoke emissions and with and without aerosol absorption. All model setups agree on net warming for the SEA dominated by the direct effect. Simulated smoke, clouds, and the direct effect agree better with observations than previous studies using the same model, though biases in aerosol extinction and liquid water path remain. Changes in cloud droplet number concentration due to BBA self-lofting influence how cleanly we can separate cloud effects into semi-direct and indirect effects. Total RE, which remains unaffected, ranges from +3.0 to +7.9 W m −2 . The 4.9 W m −2 spread arises mainly from simulated semi-direct effects. Forecasts three days long or less probably do not allow time for plausible differences in boundary layer properties due to semi-direct effects to accumulate. Free running simulations with and without smoke accumulate differences in meteorology that are likely spurious “butterfly effects”. We recommend future research quantifying BBA REs over weeks to months to use meteorological forcing techniques that allow aerosol absorption to affect the boundary layer.

Giuffrida, Eric [Carnegie Mellon University, Pitts↗

Preliminary Evaluation of GEOS-5 Aerosol and CO Distributions Forecast During TC4

The NASA Tropical Composition, Cloud, and Climate Coupling (TC4) Mission was based out San Jose, Costa Rica during July and August 2007. During TC4 the NASA Global Modeling and Assimilation Office (GMAO) ran twice-daily 0.5 x 0.666 global 5-day forecasts of the Goddard Earth Observing System atmospheric general circulation model and data assimilation system (GEOS-5). This implementation of GEOS-5 contained an aerosol and carbon monoxide (CO) model to provide online forecast tropospheric distributions of dust, sea salt, sulfate, and carbonaceous aerosols and CO for both the planning of flights and for science. Here we provide a description of the aerosol and CO modeling system and give a preliminary evaluation of forecast tracer distributions. Our comparisons to satellite observations of aerosol and CO show qualitatively similar simulated distributions of tracers to those observed. During TC4 copious amounts of dust were observed in the Caribbean. The model generally reproduced the observations of the timing of dust events and the vertical structure in the lower atmosphere. However, the model simulations had too much aerosol at high altitudes relative to airborne Cloud Physics Lidar observations. The results were similar for biomass burning aerosol and CO tracers, where the model showed higher simulated concentrations of these tracers at aircraft flight altitude than observations.

Colarco, Peter R.↗

Using Satellite‐Derived Fire Arrival Times for Coupled Wildfire‐Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season

Abstract Wildfire frequency has increased in the Western US over recent decades, driven by climate change and a legacy of forest management practices. Consequently, human structures, health, and life are increasingly at risk due to wildfires. Furthermore, wildfire smoke presents a growing hazard for regional and national air quality. In response, many scientific tools have been developed to study and forecast wildfire behavior, or test interventions that may mitigate risk. In this study, we present a retrospective analysis of 1 month of the 2020 Northern California wildfire season, when many wildfires with varying environments and behavior impacted regional air quality. We simulated this period using a coupled numerical weather prediction model with online atmospheric chemistry, and compare two approaches to representing smoke emissions: an online fire spread model driven by remotely sensed fire arrival times and a biomass burning emissions inventory. First, we quantify the differences in smoke emissions and timing of fire activity, and characterize the subsequent impact on estimates of smoke emissions. Next, we compare the simulated smoke to surface observations and remotely sensed smoke; we find that despite differences in the simulated smoke surface concentrations, the two models achieve similar levels of accuracy. We present a detailed comparison between the performance and relative strengths of both approaches, and discuss potential refinements that could further improve future simulations of wildfire smoke. Finally, we characterize the interactions between smoke and meteorology during this event, and discuss the implications that increases in regional smoke may have on future meteorological conditions.

54 ENVIRONMENTAL SCIENCES↗

Improving the Representation of Land Surface Processes using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here we apply an Ensemble Kalman Filter DA system as part of the Data Assimilation Research Testbed to a variety of land surface simulations. First, we describe the use of remotely sensed biomass observations to provide improved simulations of plant phenology, carbon and water cycling for regions highly sensitive to climate change (Western US, China, and Arctic). We discuss approaches to account for systemic biases between models and observations, including the use of spatially-varying adaptive ensemble inflation as an alternative approach to re-scaling soil moisture observations. Finally, we discuss a strategy to incorporate complementary observations (snow water equivalent, solar-induced fluorescence) to better constrain the representation of carbon and water cycling across complex terrain.

DART↗

Interannual Variations in Aerosol Sources and Their Impact on Orographic Precipitation Over California's Central Sierra Nevada

Aerosols that serve as cloud condensation nuclei (CCN) and ice nuclei (IN) have the potential to profoundly influence precipitation processes. Furthermore, changes in orographic precipitation have broad implications for reservoir storage and flood risks. As part of the CalWater I field campaign (2009-2011), the impacts of aerosol sources on precipitation were investigated in the California Sierra Nevada. In 2009, the precipitation collected on the ground was influenced by both local biomass burning (up to 79% of the insoluble residues found in precipitation) and long-range transported dust and biological particles (up to 80% combined), while in 2010, by mostly local sources of biomass burning and pollution (30-79% combined), and in 2011 by mostly long-range transport from distant sources (up to 100% dust and biological). Although vast differences in the source of residues was observed from year-to-year, dust and biological residues were omnipresent (on average, 55% of the total residues combined) and were associated with storms consisting of deep convective cloud systems and larger quantities of precipitation initiated in the ice phase. Further, biological residues were dominant during storms with relatively warm cloud temperatures (up to -15 C), suggesting these particles were more efficient IN compared to mineral dust. On the other hand, lower percentages of residues from local biomass burning and pollution were observed (on average 31% and 9%, respectively), yet these residues potentially served as CCN at the base of shallow cloud systems when precipitation quantities were low. The direct connection of the source of aerosols within clouds and precipitation type and quantity can be used in models to better assess how local emissions versus long-range transported dust and biological aerosols play a role in impacting regional weather and climate, ultimately with the goal of more accurate predictive weather forecast models and water resource management.

Creamean, J. M.↗

Online Simulations of Global Aerosol Distributions in the NASA GEOS-4 Model and Comparisons to Satellite and Ground-Based Aerosol Optical Depth

We have implemented a module for tropospheric aerosols (GO CART) online in the NASA Goddard Earth Observing System version 4 model and simulated global aerosol distributions for the period 2000-2006. The new online system offers several advantages over the previous offline version, providing a platform for aerosol data assimilation, aerosol-chemistry-climate interaction studies, and short-range chemical weather forecasting and climate prediction. We introduce as well a methodology for sampling model output consistently with satellite aerosol optical thickness (AOT) retrievals to facilitate model-satellite comparison. Our results are similar to the offline GOCART model and to the models participating in the AeroCom intercomparison. The simulated AOT has similar seasonal and regional variability and magnitude to Aerosol Robotic Network (AERONET), Moderate Resolution Imaging Spectroradiometer, and Multiangle Imaging Spectroradiometer observations. The model AOT and Angstrom parameter are consistently low relative to AERONET in biomass-burning-dominated regions, where emissions appear to be underestimated, consistent with the results of the offline GOCART model. In contrast, the model AOT is biased high in sulfate-dominated regions of North America and Europe. Our model-satellite comparison methodology shows that diurnal variability in aerosol loading is unimportant compared to sampling the model where the satellite has cloud-free observations, particularly in sulfate-dominated regions. Simulated sea salt burden and optical thickness are high by a factor of 2-3 relative to other models, and agreement between model and satellite over-ocean AOT is improved by reducing the model sea salt burden by a factor of 2. The best agreement in both AOT magnitude and variability occurs immediately downwind of the Saharan dust plume.

Colarco, Peter↗

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide (NO 2 ), formaldehyde (CH2O), sulfur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ), and aerosol optical depth (AOD). Value-added products [e.g., Particulate matter with diameters less than 2.5 micrometers (PM2.5)] are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Air Quality↗

Enhancing Air Quality Applications in the Hindu Kush-Himalayan Region Using Satellite, Model, and Machine Learning Techniques

Air pollution in the Hindu Kush Himalayan (HKH) region of South Asia is a severe issue, as increases in emissions over the past two decades have degraded air quality (AQ) across the region, which poses major threats to human health, the ecosystem, climate, and agriculture. A diversity of anthropogenic and natural emission sources including transportation, power plants, industries, open biomass burning of crop residue, forest fires, cooking and heating fires, and dust storms contribute to unhealthy AQ and transboundary pollution issues in the region. Further complicating matters is the importance of meteorology and terrain on AQ, especially in the Kathmandu Valley where extreme haze episodes frequently develop from the atmospherically stable weather conditions during the winter monsoon. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with machine learning techniques to develop a comprehensive toolkit for enhancing AQ monitoring and forecasting in HKH. The toolkit incorporates new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager (AMI), which provide unprecedented resolution on aerosols and trace gases, including nitrogen dioxide, formaldehyde, sulfur dioxide, carbon monoxide, and ozone, and aerosol optical depth. Value-added products, such as level 4 PM2.5 products, are developed from the suite of satellite observations to further improve AQ monitoring capabilities in the region. The satellite products are also used to assimilate a high-resolution chemical transport model tailored for the HKH region, which is providing daily, 54-hour AQ forecasts with horizontal grid spacings of 12- and 4-km. This presentation will provide an overview of the suite of satellite- and model-based products in the AQ toolkit and application and performance of the toolkit for AQ monitoring and forecasting in HKH.

Forecasting↗

Vertical Structure of a Springtime Smoky and Humid Troposphere Over the Southeast Atlantic From Aircraft and Reanalysis

The springtime atmosphere over the southeast Atlantic Ocean (SEA) is subjected to a consistent layer of biomass burning (BB) smoke from widespread fires on the African continent. An elevated humidity signal is coincident with this layer, consistently proportional to the amount of smoke present. The combined humidity and BB aerosol has potentially significant radiative and dynamic impacts. Here, we use aircraft-based observations from the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) deployments in conjunction with reanalyses to characterize covariations in humidity and BB smoke across the SEA. The observed plume–vapor relationship, and its agreement with the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) and Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, persists across all observations, although the magnitude of the relationship varies as the season progresses. Water vapor is well represented by the reanalyses, while CAMS tends to underestimate carbon monoxide especially under high BB. While CAMS aerosol optical depth (AOD) is generally overestimated relative to ORACLES AOD, the observations show a consistent relationship between carbon monoxide (CO) and aerosol extinction, demonstrating the utility of the CO tracer to understanding vertical aerosol distribution. We next use k-means clustering of the reanalyses to examine multi-year seasonal patterns and distributions. We identify canonical profile types of humidity and of CO, allowing us to characterize changes in vapor and BB atmospheric structures, and their impacts as they covary. While the humidity profiles show a range in both total water vapor concentration and in vertical structure, the CO profiles primarily vary in terms of maximum concentration, with similar vertical structures in each. The distribution of profile types varies spatiotemporally across the SEA region and through the season, ranging from largely one type in the northeast and southwest to more evenly distributed between multiple types where air masses meet in the middle of the SEA. These distributions follow patterns of transport from the humid, smoky source region (greatest influence in the northeast of the SEA) and the seasonal changes in both humidity and smoke (increasing and decreasing through the season, respectively). With this work, we establish a framework for a more complete analysis of the broader radiative and dynamical effects of humid aerosols over the SEA.

Kristina Pistone↗

Transport and Chemical Evolution over the Pacific (TRACE-P)Aircraft Mission: Design, Execution, and First Results

The NASA Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft mission was conducted in February-April 2001 over the NW Pacific (1) to characterize the Asian chemical outflow and relate it quantitatively to its sources and (2) to determine its chemical evolution. It used two aircraft, a DC-8 and a P-3B, operating out of Hong Kong and Yokota Air Force Base (near Tokyo), with secondary sites in Hawaii, Wake Island, Guam, Okinawa, and Midway. The aircraft carried instrumentation for measurements of long-lived greenhouse gases, ozone and its precursors, aerosols and their precursors, related species, and chemical tracers. Five chemical transport models (CTMs) were used for chemical forecasting. Customized bottom-up emission inventories for East Asia were generated prior to the mission to support chemical forecasting and to serve as a priori for evaluation with the aircraft data. Validation flights were conducted for the Measurements Of Pollution In The Troposphere (MOPITT) satellite instrument and revealed little bias (6 plus or minus 2%) in the MOPITT measurements of CO columns. A major event of transpacific Asian pollution was characterized through combined analysis of TRACE-P and MOPITT data. The TRACE-P observations showed that cold fronts sweeping across East Asia and the associated warm conveyor belts (WCBs) are the dominant pathway for Asian outflow to the Pacific in spring. The WCBs lift both anthropogenic and biomass burning (SE Asia) effluents to the free troposphere, resulting in complex chemical signatures. The TRACE-P data are in general consistent with a priori emission inventories, lending confidence in our ability to quantify Asian emissions from socioeconomic data and emission factors. However, the residential combustion source in rural China was found to be much larger than the a priori, and there were also unexplained chemical enhancements (HCN, CH3Cl, OCS, alkylnitrates) in Chinese urban plumes. The Asian source of CCl4 was found to be much higher than government estimates. Measurements of HCN and CH3CN indicated a dominant biomass burning source and ocean sink for both gases. Large fractions of sulfate and nitrate were found to be present in dust aerosols. Photochemical activity in the Asian outflow was strongly reduced by aerosol attenuation of UV radiation, with major implications for the concentrations of HOx, radicals. New particle formation, apparently from ternary nucleation involving NH3, was observed in Chinese urban plumes.

Jacob, Daniel J.↗

Application of Advanced Earth Observations and Model Simulations to Improve Air Quality Monitoring in the Hindu-Kush-Himalayan Region

Air pollution is a serious environmental health concern in the Hindu Kush Himalayan (HKH) region of south-central Asia, as rapid industrialization and population growth have led to increased anthropogenic emissions from transportation, residential, industrial, energy, and biomass burning sources. Natural emissions from dust and forest fires are additional sources of air pollutants that can exacerbate air quality in the region. The combination of the complex pollutant mixtures and atmospherically stable weather conditions during the winter monsoon can visibility reductions and hazardous air quality from persistent haze episodes. The Kathmandu Valley is especially vulnerable to extreme haze issues due to the surrounding mountains that restrict air movement and retains pollutants in the atmosphere. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with ground-based networks to provide a comprehensive data toolkit for advancing air quality monitoring and forecasting decisions in the HKH region. The toolkit includes new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager, which provide high spatiotemporal information on NO2, HCHO, SO2, O3, and aerosol optical depth (AOD). Particulate matter with diameters less than 2.5 micrometers (PM2.5) are derived from the satellite-retrieved AOD using ground-based observations and forecast model data. The satellite observations are also used to initialize and constrain forecast model systems designed for the HKH region. This talk will highlight the performance of the air quality toolkit for enhancing decision-making processes during exceptional air quality events in the region. Note: Presentation includes additional attachment of full presentation with sound and animation (best when viewed as slide show) with runtime of 15 min 32 secs

Aaron Naeger↗

Tracking Aerosol Convection Interactions Experiment (TRACER) Field Campaign Report

Convective clouds serve a critical role in the Earth’s energy and water cycles through their transport of heat, moisture, momentum, and chemical species through the troposphere driving the global circulation (e.g., Hartmann et al. 1984, Del Genio et al. 2012, Su et al. 2014). On more local scales, convective clouds impact the atmospheric heating profile through diabatic heating effects, removal of water from the atmospheric column through precipitation, and conditioning of the local environment impacting further development of clouds (e.g., Sullivan and Voigt 2021). These critical roles underscore the importance of realistic representation of convective processes across scales of models from large-eddy simulation (LES), to convection-permitting models (CPM; e.g., Kendon et al. 2020, Marinescu et al. 2021), to numerical weather prediction (NWP) models used for operational weather forecasting, to Earth system models used to predict climate sensitivity (Sanderson et al. 2011, Sherwood et al. 2014, Tomassini et al. 2014, Zhao et al. 2016, Cronin et al. 2017). A key component of improving model representation of convective clouds is better quantification and parameterization of updraft microphysics and dynamics, including their interactions with the surrounding environment and storm organization (Bony et al. 2015, Hagos and Houze 2016, Donner et al. 2016, Morrison et al. 2020). Aerosol is an important environmental factor that could affect convective clouds and precipitation since cloud droplet and ice formation processes are initiated by it. Andrae et al. (2004) hypothesized that aerosols associated with increased biomass burning particles acting as cloud condensation nuclei (CCN) result in smaller and more monodisperse cloud droplets leading to suppression of warm rain formation, ultimately leading to more cloud water being lofted above the freezing level based on observations in the Amazon region. The subsequent increase in latent heat release increases the buoyancy of rising convective parcels invigorating the deep convection. This work was followed by a description of the theoretical basis for this “cold-phase invigoration” by Rosenfeld et al. (2008), who argued that it could have a significant effect for deep convective clouds with warm cloud-bases. Several modeling studies (e.g., Khain et al. 2005, 2009, van den Heever et al. 2006, Fan et al. 2007, 2009, 2012, Lee et al. 2008, Storer et al. 2010, Lebo et al. 2012, Storer and van den Heever 2013, Chen et al. 2020, Dagan et al. 2022) have investigated these aerosol-convection interactions and the environmental factors that influence their relative importance and magnitude. More recently, several studies have indicated that “warm-phase invigoration”, the enhancement of convection through condensational heating, also appears to play a role in enhancing both shallow cumuli (Seiki and Nakajima 2014, Saleeby et al 2015) and deeper tropical convection (Lebo and Seinfeld 2011, Khain et al. 2012, Sheffield et al 2015, Fan et al. 2018, Igel and van den Heever 2021), as well as Houston thunderstorms (Fan et al. 2007, 2020). However, still other studies have provided additional evidence of systematic biases in simulated convective outflow ice size distribution properties, which are consistent with a lack of poorly understood secondary ice production within convective updrafts (e.g., Fridlind et al. 2017). To help address these critical gaps in our understanding of cloud processes, aerosol processes and aerosol-cloud interactions, the Tracking Aerosol Convection Interactions Experiment was designed building upon efforts by the Aerosol, Cloud, Precipitation and Climate (ACPC) Initiative (http://acpcintiative.org/), a joint effort of the International Geosphere-Biosphere Programme (IGBP) and the World Climate Research Program (WCRP) that focused on resolving uncertainties in the interactions between aerosol and clouds towards better understanding the role that these interactions play in the climate system. The TRACER campaign was motivated by recommendations from a number of pilot studies undertaken by ACPC (van den Heever et al. 2017, Fridlind et al. 2019, Hu et al. 2019, Fan et al. 2020, Marinescu et al. 2021, Hernandez-Deckers et al. 2022) that pointed towards the southeastern Texas region as a locale where aerosol-convection interactions could be studied owing to the copious occurrence of isolated convection during the summer months accompanied by diverse and significant sources of aerosols from both anthropogenic and natural sources. The TRACER campaign began on 01 October 2021 and extended through 30 September 2022 with an intensive operational period (IOP) during June-September 2022. Three main sites (Table 1) were managed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility.

54 ENVIRONMENTAL SCIENCES↗

Observation of Absorbing Aerosols Above Clouds over the South-East Atlantic Ocean from the Geostationary Satellite SEVIRI – Part 1: Method Description and Sensitivity

High temporal resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the South East Atlantic Ocean (SEAO). This paper presents a method developed to retrieve simultaneously aerosol and cloud properties in aerosol above cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud Aerosol Optical Thickness (AOT), the Cloud Optical Thickness (COT) and the Cloud droplet Effective Radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10% error on the humidity profile leads to an 18.5% bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00-15:00 UTC, an uncertainty of 40% is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17% between 09:00-15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6% on the COT and 3% on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time-series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above cloud events over the SEAO.

Peers, Fanny↗

Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI – Part 1: Method description and sensitivity

High-temporal-resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the south-east Atlantic Ocean (SEAO). This paper presents a method developed to simultaneously retrieve aerosol and cloud properties in aerosol above-cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT) and the cloud droplet effective radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10 % error on the humidity profile leads to an 18.5 % bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00 and 15:00 UTC, an uncertainty of 40 % is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single-scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17 % between 09:00 and 15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6 % on the COT and 3 % on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above-cloud events over the SEAO.

cloud properties↗

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Croplands play a critical role in regulating the energy and moisture exchanges between the land surface and atmosphere. However, the interactions between cropland and climate are usually poorly represented due to a lack of detailed representation in crop types and field management. Here, we coupled the Noah-MP-Crop model with the state-of-the-art Weather Research and Forecasting (WRF) model to explore and evaluate the crop growth dynamics in response to climate variations across Northeast China. The default parameters of the crop model were not exactly suitable for the agricultural ecosystems in Northeast China. The detailed cropland distribution, and crop phenology parameters including growing degree days (GDD) and planting (harvesting) date were first created using multi-source remote sensing products and reanalysis data, and was then successfully used to simulate the growth and yield for corn and soybean and associated energy exchanges. We also optimized and calibrated other crop parameters using the time-series of the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface products. The modified crop model substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%.

54 ENVIRONMENTAL SCIENCES↗

Connecting Indonesian Fires and Drought With the Type of El Niño and Phase of the Indian Ocean Dipole During 1979-2016

This study advances the previous understanding of the role of climate variability on Indonesian fire activity, by considering (i) the presence of different types of El Niño and (ii) the interaction between El Niño and the Indian Ocean Dipole (IOD). We classify 12 El Niño events during 1979-2016 into Eastern Pacific (EP) and Central Pacific (CP) types (four and eight El Niño events, respectively) and analyze observational data of sea surface temperature, precipitation, drought code, biomass burning carbon emission, visibility, and aerosol optical depth accordingly. We find that more intense and prolonged Indonesian drought and fires occur in the EP type, during which the emitted carbon amounts almost double those in the CP type. By further separating the CP type El Niño according to the phase of the IOD, that is, positive and negative, we show that fire seasons with less burning intensity and shorter duration are predominantly associated with weakly positive or even negative phase of the IOD phenomena. Moreover, fire intensity exhibits geographic diversity: fires are always more intensive in southern Kalimantan than in southern Sumatra in all El Niño events, although it is less dry in the former region. The outcome of this study can be applied to drought early warning, fire management, and air quality forecast in Indonesia and adjacent areas by identifying the type of El Niño and the phase of the IOD in advance.

IOD phenomena↗

Potential Impact of Land Use Change on Future Regional Climate in the Southeastern U.S.: Reforestation and Crop Land Conversion

The impact of future land use and land cover changes (LULCC) on regional and global climate is one of the most challenging aspects of understanding anthropogenic climate change. We study the impacts of LULCC on regional climate in the southeastern U.S. by downscaling the NASA Goddard Institute for Space Studies global climate model E to the regional scale using a spectral nudging technique with the Weather Research and Forecasting Model. Climate-relevant meteorological fields are compared for two southeastern U.S. LULCC scenarios to the current land use/cover for four seasons of the year 2050. In this work it is shown that reforestation of cropland in the southeastern U.S. tends to warm surface air by up to 0.5 K, while replacing forested land with cropland tends to cool the surface air by 0.5 K. Processes leading to this response are investigated and sensitivity analyses conducted. The sensitivity analysis shows that results are most sensitive to changes in albedo and the stomatal resistance. Evaporative cooling of croplands also plays an important role in regional climate. Implications of LULCC on air quality are discussed. Summertime warming associated with reforestation of croplands could increase the production of some secondary pollutants, while a higher boundary layer will decrease pollutant concentrations; wintertime warming may decrease emissions from biomass burning from wood stoves

Regional climate change↗

Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar

Ecological modeling and forecasting are essential tools for the understanding of complex vegetation dynamics. The parametric demands of some of these models are often lacking or scant for threatened ecosystems, particularly in diverse tropical ecosystems. One such ecosystem and also one of the world's biodiversity hotspots, Madagascar's lowland rainforests, have disappeared at an alarming rate. The processes that drive tree species growth and distribution remain as poorly understood as the species themselves. We investigated the application of the process-based individual-based FORMIND model to successfully simulate a Madagascar lowland rainforest using previously collected multi-year forest inventory plot data. We inspected the model's ability to characterize growth and species abundance distributions over the study site, and then validated the model with an independently collected forest-inventory dataset from another lowland rainforest in eastern Madagascar. Following a comparative analysis using inventory data from the two study sites, we found that FORMIND accurately captures the structure and biomass of the study forest, with r(squared) values of 0.976, 0.895, and 0.995 for 1:1 lines comparing observed and simulated values across all plant functional types for aboveground biomass (tonnes/ha), stem numbers, and basal area (m(squared)/ha), respectively. Further, in validation with a second study forest site, FORMIND also compared well, only slightly over-estimating shade-intermediate species as compared to the study site, and slightly under-representing shade-tolerant species in percentage of total aboveground biomass. As an important application of the FORMIND model, we measured the net ecosystem exchange (NEE, in tons of carbon per hectare per year) for 50 ha of simulated forest over a 1000-year run from bare ground. We found that NEE values ranged between 1 and -1 t Cha(exp -1)year(exp -1), consequently the study forest can be considered as a net neutral or a very slight carbon sink ecosystem, after the initial 130 years of growth. Our study found that FORMIND represents a valuable tool toward simulating forest dynamics in the immensely diverse Madagascar rainforests.

Forest Productivity↗