Reply on RC1

India needs a high-resolution estimation of carbon sources and sinks to implement the country’s climate change action plans and mitigation strategy effectively. Current carbon estimates over the Indian region based on the “Bottom-up” approach suffer from significant uncertainty, which calls for more process-based models and atmospheric inverse modelling to obtain a more accurate budget. Inverse models constrain the carbon fluxes based on atmospheric observation of CO2 mole fractions. The unavailability of amble observations over the Indian domain critically impacts estimation accuracy. Fortunately, there are increasing efforts to improve the availability of CO2 observation over the domain. Along with the observations, the availability of a suitable transport model to simulate the CO2 distribution is essential to the accurate inverse estimation of carbon fluxes. The inability of coarse-resolution global models to simulate the fine-scale variability in CO2 distribution warrants developing a regional high-resolution modelling system. Here we evaluate the performance of a regional high-resolution modelling system which utilises meteorological fields from the Weather Research and Forecasting (WRF) model to simulate the CO2 transport over the Indian domain using a lagrangian particle dispersion model, Stochastic Time-Inverted Lagrangian Transport Model (STILT). Using lagrangian models enables us to study the CO2 distribution at very high resolution (even at sub-grid scale) with reduced cost. We use the vegetation photosynthesis and Respiration Model (VPRM), coupled with the modelling system, to simulate the biospheric fluxes. The anthropogenic and biomass burning fluxes are obtained from different available inventories. We use CO2 in-situ observations from different parts of the Indian domain, which utilises flask measurements and PICARRO CRDS instruments, to evaluate the modelling system. Our high-resolution modelling framework shows good skill in simulating the CO2 variability over the region. The results of the evaluation will be discussed in detail during the presentation.

Methane and nitrous oxide are potent greenhouse gases whose atmospheric abundances have increased significantly in the past 200 years, together accounting for approximately half of the radiative forcing associated with increasing concentrations of carbon dioxide. In order to understand the factors causing increase of these gases globally, we need to determine their emission rates at regional to continental scales. We directly link atmospheric observations with surface emissions using a Lagrangian Particle Dispersion Model, and then determine emission rates by optimizing prior emissions estimates. We use measurements from NOAA's tall tower and aircraft program in 2004, The Stochastic Time-Inverted Lagrangian Transport model (STILT) driven by meteorological fields from a customized version of the Weather Research and Forecasting (WRF) model, and EDGAR32FT2000 and Global Emissions Inventory Activity (GEIA) as prior emission estimates. In the US and Canada, methane emission rates are found to be consistent with observations, while nitrous oxide emissions are significantly low, by a factor 2.5–3 in the peak emissions time period found to be February through May.

This paper describes the coupling between a mesoscale numerical weather prediction model, the Weather Research and Forecasting (WRF) model, and a Lagrangian Particle Dispersion Model, the Stochastic Time-Inverted Lagrangian Transport (STILT) model. The primary motivation for developing this coupled model has been to reduce transport errors in continental-scale top–down estimates of terrestrial greenhouse gas fluxes. Examples of the model’s application are shown here for backward trajectory computations originating at CO2 measurement sites in North America. Owing to its unique features, including meteorological realism and large support base, good mass conservation properties, and a realistic treatment of convection within STILT, the WRF–STILT model offers an attractive tool for a wide range of applications, including inverse flux estimates, flight planning, satellite validation, emergency response and source attribution, air quality, and planetary exploration.

Modeling the spatial and temporal variability in surface water CO2 and CH4 concentrations in a newly created complex of boreal hydroelectric reservoirs

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

<sec><p indent="0mm">The Paris Agreement has set the goal of limiting the global warming to 2°C above pre-industrial levels and established a process for global carbon stocktaking which will take place every five years starting from 2023. The nationally determined contributions (NDCs) are at the heart of the Paris Agreement, which was adopted at the UN Climate Change Conference (COP21) in 2015. Under the Paris Agreement, each country is required to present its NDCs report using the standard ‘bottom-up’ inventory approach recommended by the Intergovernmental Panel on Climate Change (IPCC). The 49th session of IPCC has suggested a ‘top-down’ inversion approach to assess NDCs using measurements of atmospheric Green House Gases (GHGs). Satellite remote sensing can measure the atmospheric GHG concentration at global scale with fine resolution, high accuracy and high frequency, providing a promising tool to validate each country’s NDCs. The European Union, the United States, Japan, and Canada are vigorously developing their satellite systems for monitoring GHGs’ concentrations and emissions. China is also planning to build a GHGs monitoring satellite system to support the second carbon stocktaking in 2028. Therefore, it is extremely urgent to review and investigate the techniques, methods and cutting-edge applications of remote sensing for GHGs monitoring and stocktaking. </sec><sec> In this paper, we first reviewed the current techniques, networks, and upcoming national or international plans for monitoring atmospheric GHGs based on ground-based, airborne, and satellite platforms. The global carbon stocktaking requires continuous and accurate observations of GHGs’ concentrations and fluxes from ground, tower-based, airborne, and spaceborne platforms. The World Meteorological Congress (WMO) approved an ambitious international project, named the Global Greenhouse Gas Watch (G3W) initiative to address key information gaps in our understanding of anthropic and natural activities on the global carbon cycle, and help parties assess the completion of GST reports. Second, we proposed the key features of the next generation carbon monitoring satellite system, which was characterized by high spatial resolution, high accuracy, high temporal frequency, and multiple gases as well as atmospheric aerosol and co-emitted polluted gases (NO₂, CO). However, it is still challenging to meet the strict accuracy requirements for the atmospheric GHGs’ concentrations, such as <sc>1 ppm</sc> for CO₂, 5 ppb for CH₄, and 1 ppb for N₂O. China’s next-generation carbon satellite is to be launched in 2026, which will greatly enhance observation capabilities, including GHG species, accuracy, time frequency and spatial resolution. Third, taking the Global Carbon Project (GCP) series of assessment reports as the core references, we summarized the current methods, progress and challenges in the scientific assessment of GHGs’ emissions at global or regional scales. We also investigated the roles and current capabilities of satellite remote sensing and assimilation inversion technology in monitoring emissions of the three major greenhouse gases: CO₂, CH₄, and N₂O. At present, there are large uncertainties among the estimated GHGs’ fluxes (both emissions and sinks) due to very limited observations, model accuracy, and coarse resolution of assimilation systems etc. Finally, we investigated China’s demand for GHGs monitoring and emission assessment, and proposed three urgent tasks for global carbon stocktaking using the ‘top-down’ remote sensing method. These tasks include developing a satellite constellation for high-precision GHGs observation, a high-resolution, multi-species global GHG assimilation system, and an operational platform to assess global GHG emissions from terrestrial ecosystems and anthropogenic activities. Therefore, this paper, focusing on the needs of compliance with climate change and the “dual-carbon” targets, provides ideas for the construction of China’s next generation carbon-sensing satellite system. </sec>

Assimilation of accurate external satellite-based water vapor data has been demonstrated to be able to improve the Numerical Weather Prediction (NWP) model&amp;#8217;s forecasting performance. In this study, two types of near-infrared Precipitable Water Vapor (PWV) data from the Ocean and Land Colour Instrument (OLCI) of the Sentinel-3A and -3B satellites are assimilated into the Weather Research and Forecasting (WRF) model to enhance the weather forecasting performance over the South China area. One type is the raw PWV products obtained directly from the Sentinle-3A and -3B satellites; the other type is the Sentinel-3A and -3B PWV calibrated by PWV from Global Navigation Satellite System (GNSS). The Back Propagation Neural Network (BPNN) model is used in the PWV calibration.&amp;#160;Correspondingly, two WRF assimilation schemes are adopted to assimilate the Sentinel-3 raw PWV and Sentinel-3 calibrated PWV. Additionally, the WRF without data assimilation scheme is used as the background run to investigate the impact of data assimilation. Three WRF experimental schemes are performed for two periods, i.e., February 2020 (dry period) and July 2020 (wet period). The forecasting performance of these three WRF schemes are evaluated by observations from GNSS, radiosonde, and surface meteorological stations. The comparison results with GNSS PWV show that assimilation of Sentienl-3 calibrated PWV can improve PWV forecasting accuracy by 5.6% for February period for the first 6 h after data assimilation. Evaluated by radiosonde profiles, the assimilation of Sentinel-3 both raw and calibrated PWV can improve WRF humidity and temperature forecasting accuracy over the July period. The accuracy improvement can be up to 9.2% (at 2 km to 3 km of altitude) for water vapor mixing ratio and 6.0% for temperature (at 4 km to 5 km of altitude). The WRF rainfall evaluation result has been validated by rainfall observations from meteorological stations and it shows that the assimilation of Sentinel-3 PWV can improve the rainfall forecasting performance over the July period. The rainfall forecasting success rates of three schemes of assimilating no data, Sentinel-3 raw PWV, and Sentinel-3 calibrated PWV are 69.4%, 69.6%, and 71.1%, respectively, over the July 2020 in the South China region.

Prediction of tropical cyclones (TCs) beyond a week is challenging but of great importance for disaster prevention and mitigation. We propose a hybrid machine learning (ML)/physics‐based modeling framework to extend TC forecasts to 2 weeks. This framework integrates a recently launched ML‐based global weather prediction model (Pangu) and the high‐resolution physics‐based regional weather research and forecasting (WRF) model. The Pangu model shows promise in enhancing the accuracy of predictions for large‐scale circulation and TC tracks, while the high‐resolution WRF model is capable of capturing the core processes underlying TC evolution. To capitalize on the complementary strengths of both the Pangu and WRF models in predicting TCs, the framework comprises three key components: downscaling the Pangu model using the WRF model, adjusting large‐scale circulation through spectral nudging driven by the Pangu model forecasts, and updating sea surface temperature using an ocean mixed‐layer model. These components also ensure the framework's feasibility for real‐time TC forecasting. The prediction skill of the framework has been demonstrated for five long‐lived TCs across various basins from 2018 to 2023. Results indicate that the hybrid ML/physics‐based modeling framework decreased the 2‐week mean TC track and intensity errors by 59% and 32% compared to the global numerical weather prediction models, by 2% and 59% compared to the ERA5‐driven Pangu model, and by 32% and 23% compared to the ERA5‐driven WRF model, respectively. This implies that the framework has great potential to be used for 2‐week extended prediction of TCs.

Abstract. Top–down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in combination with the Weather Research and Forecasting (WRF) model in a regional Bayesian synthesis inversion. The regional Lagrangian particle dispersion model framework is well suited to make use of ASCENDS observations to constrain weekly fluxes in North America at a high resolution, in this case at 1° latitude × 1° longitude. We consider random measurement errors only, modeled as a function of the mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the grid scale and weekly resolution, the largest uncertainty reductions, on the order of 50%, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 μm candidate wavelength than for a 2.05 μm wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual biome scale range from ~40% to ~75% across our four instrument design cases and from ~65% to ~85% for the continent as a whole. Tests suggest that the quantitative results are moderately sensitive to assumptions regarding a priori uncertainties and boundary conditions. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr−1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

Four dynamical downscaling simulations are performed with different combinations of land cover maps and greenhouse gas (GHG) levels using the Weather Research and Forecasting (WRF) model nested in the Community Earth System (CESM) model. A pseudo-global warming downscaling method is used to effectively separate the anthropogenic signals from the internal noises of climate models. Based on these simulations, we investigate the impacts of anthropogenic increase in GHG concentrations and land use and land cover change (LULCC) on mean climate and extreme events in the arid and semi-arid regions of China. The results suggest that increased GHG concentrations lead to significant increases in the surface air temperature at 2 m height (T2m) by 1–1.5 °C and greater increase in the warm day temperature (TX90p) than the cold day temperature (TX10p) in the arid and semi-arid regions. Moreover, precipitation increases by 30–50% in the arid region in cold season (November to March) due to the GHG-induced increase in moisture recycling rate and precipitation efficiency. LULCC leads to significant decreases in the T2m, TX90p, and TX10p by approximately 0.3 °C. The regional LULCC accounts for 66 and 68% decrease in T2m in warm and cold seasons, respectively. The rest changes in T2m results from the changes in lateral boundary condition induced by the global LULCC. In response to LULCC, both the warm and cold day temperatures show a significant decrease in cold seasons, which primarily results from the regional LULCC. LULCC-induced changes in precipitation are generally weak in the arid and semi-arid regions of China.

Numerical weather prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are increasingly used over the Indian region to forecast extreme rainfall events. However, studies which explore the application of high-resolution rainfall simulations obtained from the WRF model in urban hydrology are limited. In this paper, the utility of a model coupling framework to predict urban floods is explored through the case study of Bangalore city in India. This framework is used to simulate multiple extreme events that occurred over the city for the monsoons of years 2020 and 2021. To address the uncertainty from the WRF model, a 12-member convection permitting ensemble is used. Model configurations using Kain Fritsch and WSM6 parameterization schemes could simulate the spatial and temporal pattern of the selected event. The city is easily flooded with rainfall events above a threshold of 60 mm/day and to capture the response of the urban catchment, the Personal Computer Storm Water Management Model (PCSWMM) is used in this study. Flood forecasts are created using the outputs from the WRF ensemble and the Global Forecasting System (GFS). The high temporal and spatial resolution of the rainfall forecasts (&amp;lt;4 km at 15-min intervals), has proved critical in reproducing the urban flood event. The flood forecasts created using the WRF ensemble indicate that flooding and water levels are comparable to the observed whereas the GFS underestimates these to a large extent. Thus, the coupled WRF–PCSWMM modelling framework is found effective in forecasting flood events over an Indian city.

Abstract. Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula and its highest peaks in central Italy. Using the latter as our area of interest and the snow seasons 2018/19, 2019/20 and 2020/21, the goal of this study is to investigate the ability of a simple single-layer and a more sophisticated multi-layer snow cover numerical model to reproduce the observed snow height, snow water equivalent and snow extent in the central Apennines, using for both models the same forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: (i) Noah LSM, an Eulerian model which simulates the snowpack as a bulk single layer, and (ii) Alpine3D, a multi-layer Lagrangian model which simulates the snowpack stratification. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at a regional scale with a spatial resolution of 3 km. While Noah LSM is already online-coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients higher than 0.68, except for wind speed, which suffered from the model underestimation of the real elevation. The performances of both WRF–Noah and WRF–Alpine3D are evaluated by comparing simulated and measured snow height, snow height variation and snow water equivalent, provided by a quality-controlled network of automatic and manual snow stations located in the central Apennines. We find that WRF–Alpine3D can predict better than WRF–Noah the snow height and the snow water equivalent, showing a correlation coefficient with the observations of 0.9 for the former and 0.7 for the latter. Both models show similar performances in reproducing the observed daily snow height variation; nevertheless WRF–Noah is slightly better at predicting large positive variations, while WRF–Alpine3D can slightly better simulate large negative variations. Finally we investigate the abilities of the models in simulating the snow cover area fraction, and we show that WRF–Noah and WRF–Alpine3D have almost equal skills, with both models overestimating it. The equal skills are also confirmed by Jaccard and the average symmetric surface distance indices.

This research addressed an important DOE science area: the role of natural disturbances in carbon cycling. Our project included both (a) modeling and field measurements and (b) strengthened ties between DOE national laboratories and universities which continue after the project was completed. Our focus was on understanding the consequences of drought and emissions (fire and urban) to atmospheric trace gas composition (carbon dioxide, carbon monoxide, and methane), including both concentration and stable isotope composition. Measurements of these atmospheric gases were used to infer the spatial and temporal patterns of both sources and sinks in the carbon cycle. As part of this effort, we maintained two long-term monitoring networks along a geographic gradient in Utah and Colorado that spans montane forests, urban regions, and oil/gas fields. In this research, we expanded on a well-accepted atmospheric model (STILT-WRF) as a tool to evaluate fluxes in CLM/CESM-related models. We used the STILT model to provide a strong linkage between point atmospheric measurements and the surface parameterizations/emissions that are part of the CLM/CESM models. Overall, this project benefited and supported the DOE Long Term Mission and Goals in four distinct ways: (a) testing of carbon cycle models, (b) testing and evaluating model mechanisms whereby factors in CLM influence trace gas composition in CESM, (c) acquiring high-quality, long-term data on concentrations of carbon dioxide in western USA ecosystems, and (d) by reducing uncertainties associated with the representation of climate-carbon feedbacks in Earth System models through the development of new methods for evaluating model performance. One postdoctoral associate (Henrique Duarte) and one Ph.D. student (Darek Malia) were trained with this support. Nine peer-reviewed publications and 14 poster presentations were produced with support from this project. This proposal addresses a specific science area in the RFP: the role of natural disturbances in carbon cycling. In particular, we will focus on the consequences of drought and fire at landscape to regional scales for atmospheric trace gas concentration and stable isotope composition (CO2, CO, and CH4). We seek to expand on the well accepted atmospheric Stochastic Time-Inverted Lagrangian Transport (STILT) model coupled to the Weather Research and Forecasting (WRF) model as a tool to evaluate stable isotope fluxes in CLM/CESM-related models through existing, long-term data sets and field campaigns where high-precision trace gas concentration and isotope measurements can be made along gradients and transects. Given the confounding impacts of anthropogenic trace gas emissions on landscape-to-regional atmospheric observations, we will also measure and parameterize isotopic characteristics of CO2, CO, and CH4 from anthropogenic or forest sources in order to allow us the opportunity to partition atmospheric trace gases into natural and anthropogenic sources.

Snow meltwater (snowmelt) that seeps into the subsurface is a major factor contributing to the development of landslides during the spring in mountainous areas of the Rocky Mountain region. An examination of historical temperature data in relation to spring season landslide occurrences reveals an association between the landslide events and intervals of rising temperatures that accelerate the production of snow meltwater. Historical climatic data recorded at local weather stations located near the landslide sites are used to show the association and to identify a temperature threshold that may be useful for forecasting the onset of spring season landslides. Historical daily temperature maximums and minimums for unmonitored landslide sites are estimated by applying an elevation correction factor to historical temperature data from nearby weather stations. The proposed temperature threshold (a 6-day moving average of daily maximum temperature of 58 F) is defined by the number and temporal distribution of snowmelt related landslide events. The results of the study suggest that real-time temperature data recorded at weather stations throughout the Rocky Mountain region is potentially a valuable source of information that may be useful for forecasting the onset of spring season landslides.

Understanding the features of weather system around and over the Arabian Gulf is a prerequisite for setting up of a numerical weather forecasting system. The reanalysis, ERA-Interim data with a spatial resolution of ∼80 km has been utilized for analyzing the wind climatology for a period of 35 year. It is found that in the Arabian Gulf, throughout the year, the predominant wind is from north-west and the average wind speed is high in the summer between June and August. The high wind speed systems in summer are known as summer Shamals and have a typical duration of one week. Often, the high speed and long duration of the Summer Shamals makes it a good carrier of sand/dust. The winter season is dominated by south-east passage of frontal systems. The presence of land-sea contrast can cause mesoscale wind systems such as land-sea breeze. Qatar being a narrow peninsular region with width less than 100 km, the setting up of these land-sea breeze systems can be from either of the sides. This can lead to the interaction of two sea breezes from opposite sides, which may lead to the development of a convergence zone. This complex systems can affect the local weather and air-quality of coastal areas. These mesoscale phenomena and complexities are not well represented through a global model. Though the low resolution (80 km) data (ERA-interim) shows the presence of Shamal winds, the data misses the detail structure and their spatial variability. This necessitates for setting up of a high resolution mesoscale simulation approach.A non-hydrostatic mesoscale model, Weather Research and Forecast (WRF) developed by NCAR (Skamarok et al 2008) is used here for simulating and forecasting the wind systems associated with the Arabian Gulf. The model is run on an operational basis to forecast the wind fields, for a 48 hr. The modeling suite consists of a preprocessor, WPS, the model, WRF and a post processor, ARWpsot. The total suite has been compiled and installed successfully using FORTRAN and C compilers. A two way nested WRF domains with a grid size ratio of 1:3 is configured. The resolutions for the outer domain and inner domain are set to 9 km, and 3 km respectively. Various physical parametrization schemes are available within WRF modelling system. The physical parameterization schemes used in the current configuration includes MYJ boundary layer parametrization, Janjic Eta Monin–Obukhov surface layer scheme, Dudhia shortwave radiation, Rapid Radiative Transfer Model (RRTM) long wave radiation, WRF Single-Moment 6-Class (WSM6) microphysics, and the NOAH land surface scheme. Cumulus scheme, Grell is used only for the outer domain. Both of the model domains have a total of 39 vertical levels, with the topmost level at 50 hPa and lowermost level at approximately 30 m above the ground level. The initial and boundary conditions are obtained from the National Centers for Environmental Prediction's (NCEP) Global forecast System (GFS) available at resolution 0.50 (∼50 km), and the boundary conditions are updated at 6 hr interval. The entire process is automatized and run on RAAD Linux HPC cluster computing system.A two day simulation of WRF is analyzed and the sea breeze system of Qatar peninsula is examined and compared with the automatic weather station (AWS) data of Qatar Meteorological Department. The time of onset and duration of sea breeze are identified. The simulation of WRF clearly shows the development of a thermal internal boundary layer over the coastal city Doha. The existence of a convergence zone with high vertical updraft was also found. This may be due to the interaction of sea breezes coming from opposite sides of Qatar peninsula. Understanding the system of sea breeze would benefit the overall understanding pollutant transport and the dispersion mechanisms.