Climate Impact Analysis Research Articles

Hydrological response to climate change in Baro basin, Ethiopia, using representative concentration pathway scenarios

Droughts and floods are common in the Baro basin and climate change may exacerbate them. This study aimed to investigate the hydrological response to climate change’s impact in the Baro River basin. Four climate models namely, Hadley Centre Global Environmental Model, version 2 (HadGEM2-ES), Max Planck Institute Earth System Model—Low Resolution (MPI-ESM-LR), Coupled Model Version 5, Medium Resolution (CM5A-MR) and European Community Earth System Model (EC-Earth) dynamically downscaled outputs were obtained from Africa coordinated regional downscaling experiment program. The four climate models were evaluated using a suite of statistical measures such as bias, Root Mean Squared Error, and Coefficient of Variation. The bias of the simulated rainfall varies between − 4.20% and − 25.39% suggesting underestimation. The performance of the models differs subject to the performance measures used for evaluation. Before being used in the climate impact analysis, the climate model data was heavily skewed and needed correction. In terms of bias, HadGEM2-ES performed the worst while EC-Earth performed the best. MPI-ESM-LR was the worst performer in terms of RMSE and CM5A-MR was the best. Changes in the hydrological response to climate change were compared to the baseline scenario (1971–2000) under the Representative Concentration Pathway Scenarios (RCP 4.5) for the medium term (2041–2070). The GCM predictions for the RCP 4.5 scenarios suggested that, in the medium period (2041–2070) the maximum temperature in the Baro River basin will probably rise by 2.1 °C for MPI-ESM-LR and 2.49 °C for CM5A-MR, while the minimum temperature would likely climb by 1.7 °C to EC-Earth and 2.8 °C for HadGEM2-ES. Annual rainfall is expected to fall by 7.02% for CM5A-MR and 17.01% for HadGEM2-ES, while annual evapotranspiration potential is likely to rise. Except from March to May CM5A-MR consistently generated the greatest amount of streamflow change, while MPI-ESM-LR consistently generated the highest magnitude of streamflow change. The annual streamflow reduction is consistent with the annual precipitation reduction and increased annual potential evapotranspiration. Generally, climate change is predicted to have a significant impact on the hydrological response in the Baro River basin under the RCP 4.5 scenario.

Towards climate neutrality in the Spanish N-fertilizer sector: A study based on radiative forcing

Agricultural systems in the 21st Century face the double challenge of achieving climate neutrality while maintaining food security. Synthetic fertilizers rich in nitrogen (N-fertilizers) boost agricultural production at the expense of increasing climate impact. Public policies, such as the Farm-to-Fork (F2F) Strategy, aim to reduce the extensive use of N-fertilizers with the ultimate goal of achieving a climate neutral European Union (EU). However, the strong link between N-fertilizers and GHG emissions (i.e., CO2, CH4 and, especially, N2O) highlights the need to better understand the climate impact of this sector. The present study conducts a climate impact analysis of Spanish N-fertilizer sector for two periods: (i) from 1960 to 2020 using real data and (ii) from 2021 to 2100 considering five forecasted scenarios. The scenarios range from business-as-usual practices to a full accomplishment of the goals pursued by the EU's F2F strategy. The system's climate stability and neutrality are analysed for the different scenarios based on radiative forcing (RF) metrics. Additionally, the study evaluates the short-term impact of the EU decarbonization goals on the climate impact of the Spanish N-fertilizer sector. The results of the study illustrate that the long-lasting climate impact of N2O and CO2 emissions compromise the capacity of N-fertilizer sector to achieve climate stability and approach climate neutrality. However, the decarbonisation of transport and N-fertilizer production activities is an important driver to substantially reduce the life cycle CH4 and CO2 emissions in the Spanish N-fertilizer sector. The results also highlight that more severe reductions on N-cycles than those suggested by the EU's F2F are required, especially to reduce the long-lasting N2O emissions in the N-fertilizer sector. Overall, the study concludes that using RF-based metrics increases robustness and transparency of climate assessments, which is necessary for a higher integration of climate science within public policymaking.

Projecting traffic flows for road-based passenger transport in Europe for the analysis of climate impact

Road-based transport is a significant contributor to global transport emissions of greenhouse gases and local pollutants, thus contributing to the environmental impact of the mobility of people and goods. In order to develop strategies to mitigate this impact, it is necessary to build reliable emission inventories that include sector-specific emissions. Furthermore, the methods for creating these inventories should be applicable for forecasts and scenario calculations to facilitate the evaluation of pathways towards a more sustainable transport system. The study at hand proposes a model-based framework to predict travel demand and its spatial distribution for a bottom-up calculation of road transport emissions in Europe. With this framework, it is possible to calculate emissions based on the road network structure, traffic flows and vehicle types. In order to demonstrate the applicability of the framework for scenario calculations, it is applied to three exemplary scenarios where population data is modified. With the developed methodology, a tool for the large-scale assessment of emissions from road transport is provided, which is able to simulate the impact of socio-economic and economic changes on these emissions.

More Frequent Abrupt Marine Environmental Changes Expected

AbstractWe quantify an elevated occurrence of abrupt changes in ocean environmental conditions under human‐induced climate forcing using Earth system model output through a novel analysis method that compares the temporal evolution of the forcings applied with the development of local ocean state changes for temperature, oxygen concentration, and carbonate ion concentration. Through a multi‐centennial Earth system model experiment, we show that such an increase is not fully reversible after excess greenhouse gas emissions go back to zero. The increase in occurrence of regional abrupt changes in marine environmental conditions has not yet been accounted for adequately in climate impact analyses that usually associate ecosystem shifts large‐scale variability or extreme events. Estimates for remaining greenhouse gas emission targets need thus to be more conservative.

The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis

Abstract. Climate models provide the required input data for global or regional climate impact analysis in temporally aggregated form, often in daily resolution to save space on data servers. Today, many impact models work with daily data; however, sub-daily climate information is becoming increasingly important for more and more models from different sectors, such as the agricultural, water, and energy sectors. Therefore, the open-source Teddy tool (temporal disaggregation of daily climate model data) has been developed to disaggregate (temporally downscale) daily climate data to sub-daily hourly values. Here, we describe and validate the temporal disaggregation, which is based on the choice of daily climate analogues. In this study, we apply the Teddy tool to disaggregate bias-corrected climate model data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We choose to disaggregate temperature, precipitation, humidity, longwave radiation, shortwave radiation, surface pressure, and wind speed. As a reference, globally available bias-corrected hourly reanalysis WFDE5 (WATCH Forcing Data methodology applied to ERA5) data from 1980–2019 are used to take specific local and seasonal features of the empirical diurnal profiles into account. For a given location and day within the climate model data, the Teddy tool screens the reference data set to find the most similar meteorological day based on rank statistics. The diurnal profile of the reference data is then applied on the climate model. The physical dependency between variables is preserved, since the diurnal profile of all variables is taken from the same, most similar meteorological day of the historical reanalysis dataset. Mass and energy are strictly preserved by the Teddy tool to exactly reproduce the daily values from the climate models. For evaluation, we aggregate the hourly WFDE5 data to daily values and apply the Teddy tool for disaggregation. Thereby, we compare the original hourly data with the data disaggregated by Teddy. We perform a sensitivity analysis of different time window sizes used for finding the most similar meteorological day in the past. In addition, we perform a cross-validation and autocorrelation analysis for 30 globally distributed samples around the world that represent different climate zones. The validation shows that Teddy is able to reproduce historical diurnal courses with high correlations >0.9 for all variables, except for wind speed (>0.75) and precipitation (>0.5). We discuss the limitations of the method regarding the reproduction of precipitation extremes, interday connectivity, and disaggregation of end-of-century projections with strong warming. Depending on the use case, sub-daily data provided by the Teddy tool could make climate impact assessments more robust and reliable.

A Framework for Climate Resilient Urban Design: The Case of Porte de Montreuil, Paris

With the increasing frequency and intensity of extreme climate events in cities, it is essential to develop multi-scale and multi-hazard design tools to ensure urban climate resilience. A designed approach to urban development across spatial scales offers the opportunity to integrate diverse fields to create a strong multidisciplinary knowledge base and avoid fragmented planning approaches. This paper proposes a process-based methodological framework for climate resilient urban design-integrating analysis of climate impact with concerns of local communities. A combined focus on climate impact and co-benefits enables a design process with the ability to promote adaptation and mitigation while also addressing diverse urban challenges and responding to local needs and priorities. The proposed methodological framework is applied in the context of the climate resilient urban redevelopment of the Porte de Montreuil district in Paris, France. The results show that the Porte de Montreuil area is at risk from heat waves as a result of the urban characteristics of the area. However, it is possible to suggest specific design measures that integrate local planning priorities with climate resilient design measures to decrease the risk and improve climate resilience in the area.

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.

Climate Impact and Feasibility Analysis of Global Green GDP Based on Improved Weight Method and K-Means Method

Gross domestic product (GDP) is a widely used economic indicator, but it does not consider economy related to natural resources. It may not accurately reflect a country's economic well-being. In this study, the potential of using "green GDP" (GGDP) and the GGDP index as primary indicators of economic health with a focus on climate implications is explored. The article proposes a new GGDP model and index that incorporate three key factors including environment, economy, and society with four quantifiable indicators within each factor. The weights for the indicators are calculated based on improved weight method containing entropy weight method and CRITIC method. GGDP model places emphasis on climate mitigation and employs grey correlation analysis and univariate linear regression. Furthermore, the GGDPGI model has been developed to assess the feasibility of replacing GDP with GGDP as the primary measure of economic growth. Our results suggest that using GGDP and the GGDP index as primary indicators is not only feasible but also meaningful for evaluating a country's economic growth.

Estimation and Climate Impact Analysis of Terrestrial Vegetation Net Primary Productivity in China from 2001 to 2020

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments.

Thawing permafrost poses environmental threat to thousands of sites with legacy industrial contamination

Industrial contaminants accumulated in Arctic permafrost regions have been largely neglected in existing climate impact analyses. Here we identify about 4500 industrial sites where potentially hazardous substances are actively handled or stored in the permafrost-dominated regions of the Arctic. Furthermore, we estimate that between 13,000 and 20,000 contaminated sites are related to these industrial sites. Ongoing climate warming will increase the risk of contamination and mobilization of toxic substances since about 1100 industrial sites and 3500 to 5200 contaminated sites located in regions of stable permafrost will start to thaw before the end of this century. This poses a serious environmental threat, which is exacerbated by climate change in the near future. To avoid future environmental hazards, reliable long-term planning strategies for industrial and contaminated sites are needed that take into account the impacts of cimate change.

Lifecycle climate impact and primary energy use of electric and biofuel cargo trucks

AbstractHeavy trucks contribute significantly to climate change, and in 2020 were responsible for 7% of total Swedish GHG emissions and 5% of total global CO2emissions. Here we study the full lifecycle of cargo trucks powered by different energy pathways, comparing their biomass feedstock use, primary energy use, net biogenic and fossil CO2emission and cumulative radiative forcing. We analyse battery electric trucks with bioelectricity from stand‐alone or combined heat and power (CHP) plants, and pathways where bioelectricity is integrated with wind and solar electricity. We analyse trucks operated on fossil diesel fuel and on dimethyl ether (DME). All energy pathways are analysed with and without carbon capture and storage (CCS). Bioelectricity and DME are produced from forest harvest residues. Forest biomass is a limited resource, so in a scenario analysis we allocate a fixed amount of biomass to power Swedish truck transport. Battery lifespan and chemistry, the technology level of energy supply, and the biomass source and transport distance are all varied to understand how sensitive the results are to these parameters. We find that pathways using electricity to power battery electric trucks have much lower climate impacts and primary energy use, compared to diesel‐ and DME‐based pathways. The pathways using bioelectricity with CCS result in negative emissions leading to global cooling of the earth. The pathways using diesel and DME have significant and very similar climate impact, even with CCS. The robust results show that truck electrification and increased renewable electricity production is a much better strategy to reduce the climate impact of cargo transport than the adoption of DME trucks, and much more primary energy efficient. This climate impact analysis includes all fossil and net biogenic CO2emissions as well as the timing of these emissions. Considering only fossil emissions is incomplete and could be misleading.

The cold regions hydrological modelling platform for hydrological diagnosis and prediction based on process understanding

Cold regions involve hydrological processes that are not often addressed appropriately in hydrological models. The Cold Regions Hydrological Modelling platform (CRHM) was initially developed in 1998 to assemble and explore the hydrological understanding developed from a series of research basins spanning Canada and international cold regions. Hydrological processes and basin response in cold regions are simulated in a flexible, modular, object-oriented, multiphysics platform. The CRHM platform allows for multiple representations of forcing data interpolation and extrapolation, hydrological model spatial and physical process structures, and parameter values. It is well suited for model falsification, algorithm intercomparison and benchmarking, and has been deployed for basin hydrology diagnosis, prediction, land use change and water quality analysis, climate impact analysis and flood forecasting around the world. This paper describes CRHM’s capabilities, and the insights derived by applying the model in concert with process hydrology research and using the combined information and understanding from research basins to predict hydrological variables, diagnose hydrological change and determine the appropriateness of model structure and parameterisations.

Challenges in reanalysis products to assess extreme weather impacts on agriculture: Study case in southern Sweden

The incidence of dry or wet day sequences has a great influence on crops management and development. The lack of spatialized observed data with appropriate temporal resolution to investigate the changes that has occurred during the last century regarding the length and frequencies of those sequences has led to reliance on reanalysis products. However, the question can be raised about the suitability of those products when evaluating such climate indices and their impacts on crop production. Different products are here investigated to evaluate how the succession of dry and wet days are depicted in Sweden. Results show that reanalysis product tends to overestimate the number of wet days and wet periods and underestimate dry periods. We also showed clearly that the frequency and intensity of dry and wet spells returned can differ widely between products. For instance, number of dry spell events can range from 1 to 11 over the same decade for two different products. This paper does not aim to classify the RPs regarding their goodness or efficiency but try to highlights the divergence between them in representation of spells which could generate substantial differences in climate impact analysis in agricultural modeling.

Improved representativeness of simulated climate using natural units and monthly resolution

There is a considerable discrepancy between the temporal and spatial resolution required by climate impact researchers, policy makers, and adaptation planners on the one hand and climate data providers on the other hand. While the spatial and temporal aggregation of climate data is necessary to increase the reliability and robustness of climate information, this often counteracts or even prohibits their use in adaptation planning. The problem is twofold (i.e., space and time) and needs to be approached accordingly. Climate impact research and adaptation planning are the domain of impact experts, politicians, and planners, rather than climate experts. Thus, besides the spatial and temporal resolution, information also needs to be provided on platforms and in data formats that are easily accessible, easy to handle, and easy to understand. We discuss possible steps toward bridging the gap using an example from the federal state Hesse (Germany) as illustration. We aggregate the climate data at a level of “natural units” and provide them as monthly data. We discuss the pros and cons of this kind of processed data for impact research and decision making. The spatial aggregation to “natural units” delivers suitable spatial aggregation, while maintaining physical geographic structures and their climatic characteristics. Within these “natural units,” single grid cell values are usable for climate impact analyses or decision making. The temporal resolution is monthly values, i.e., deviations of single month values for the scenario period from climatological monthly values in the (simulated) reference period. This resolution allows analyzing compound events or consecutive events on a monthly scale within a climatological (30-year) period.

Climate Impact and Feasibility Analysis of Incineration Based Waste-to-Energy Plants in Delhi

Managing the increasing municipal solid waste (MSW) generated in cities across India has increasingly become a challenge for Pollution Control Boards and Civic Authorities. The increasing population of Delhi has led to overflowing landfills that contaminate groundwater and release tonnes of greenhouse gases. Lately, waste to energy (WtE) plants have emerged as the preferred option in Delhi for waste processing but their adoption has been marred with public disapproval, emission violations and below-par energy production. This study quantifies the climate impact and investigates the financial feasibility of incineration based WtE units in Delhi. The Climate Impact estimates the Greenhouse Gas emissions of WtE Incinerators and compares them with the GHG emissions avoided via Electricity Generation and Landfilling. For Feasibility Analysis, the cost and revenue streams data were estimated to find out Net Present Values for the project lifecycle and Break-even Periods. The climate impact of all 3 WtE plants came out to be positive, with the Bawana WtE leading at 468,041 tonnes of CO2-eq release. The Net Present Value after 20 years for Ghazipur, Bawana and Okhla WtE was at INR 594 million, 7541 million and 8965 million and break-even period was 12, 6, and 3 years, respectively. We conclude the study with policy recommendations & technical improvements aimed at improving feasibility, increasing renewable energy outputs and reducing the GHG emissions of WtE plants.

Climate Impact Assessment for Sustainable Structural Change in the Rhenish Lignite Mining Region

The Rhenish lignite mining region is facing enormous structural changes due to the withdrawal from opencast mining. The current planning of the regional transformation process, however, has so far only insufficiently considered the local impacts of climate change and thus also existing needs for action as well as opportunities in the context of sustainable regional development. However, the integrated consideration of these challenges is probably one of the biggest strategic challenges for this region. The aim of our study was therefore to apply a method for the integrated analysis of structural and climatic changes in the context of a climate impact assessment. We follow a parallel spatial modelling approach and use scenario corridors to describe the bandwidth of potential future conditions. The results clearly show the influence of other drivers such as changes in population and land use, and thus the adaptation options within the context of a sustainable transformation process. Structural changes should be considered in the context of climate impact analyses, as well as climate changes in the management of structural change.

Pansharpened landsat 8 thermal-infrared data for improved Land Surface Temperature characterization in a heterogeneous urban landscape

Reliable urban Land Use Land Cover (LULC) and Land Surface Temperature (LST) characterization are important for land use planning as well as climate and thermal impact analysis in cities. High resolution LULC and LST datasets are required for detailed spatial analysis such as understanding the contribution of small LULC fragments to thermal characteristics of the entire urban landscape. To date, detailed analysis of urban surfaces using satellite imagery has been hindered by low to moderate spatial resolution of freely available sensors of past and existing missions. Hence, this study sought to investigate the potential of pansharpening Landsat 8 data in improving LULC mapping and LST characterization in Bulawayo metropolitan city, Zimbabwe. Six LULC classes namely; bare area, built-up, low density, dense forest, grasslands and water bodies were considered for the study and respective spatially-explicit locational information (GPS coordinates) class samples identified. Supervised image classification using the Support Vector Machines algorithm was adopted for the un-pansharpened (30m) and separately using different pansharpened (i.e. Brovey, Simple Mean, ESR, IHS and Gram-Schmidt) datasets (15m) as input. Results indicated improved discriminability of LULC and LST features as well as increased classification accuracy from all pansharpening methods when compared to the 30m resolution data. Average inter-class separability increased from 1.577 using original data to 1.863 after pansharpening. The simple mean method showed the highest classification accuracy (92.13%), outperforming un-pansharpened data (89.77%) and other pansharpening methods (89.45–91.75%). Pansharpened data showed better accuracy in LST spatial characterization than un-pansharpened thermal data. For each LULC class, mean LST was slightly higher before, than after pansharpening, by between 0.1 and 1 °C. The study further established that pansharpening based on simple mean method did not compromise the spatial correlation between LULC and LST. These findings present a great improvement towards accurately mapping LULC and LST in heterogeneous urban landscapes.

Impacts of river flooding on urban agglomerations: a climate-impact analysis and the role of strategic planning for the Ruhr, Germany

In order to reveal the present and future flooding impacts on residential and mixed-use areas, a parallel modelling approach (PMA) was carried out. With the use of geo-information systems (GIS), temporal and spatial impacts can be quantified and visualised, while the results of the proposed method justify adequate regional planning strategies for the Ruhr, Germany. As an information system which is regularly updated within a cooperation process between the municipalities concerned, the so called ruhrFISְ of the Ruhr Regional Association (RVR) seems to be a promising approach to improve the evidence basis for adaptation actions.

Temporal Scaling of Streamflow Elasticity to Precipitation: A Global Analysis

AbstractStreamflow elasticity to precipitation, defined as the percent change of streamflow resulting from a 1% change in precipitation, is sometimes used as an alternative to rainfall‐runoff models in climate impact analyses. Elasticity is usually estimated from long streamflow and precipitation series aggregated at annual time steps while the climate impact analyses are usually geared toward changes at decadal scales. The purpose of this paper is therefore to understand how the elasticity depends on the aggregation time scale and the process controls of such a dependence. We analyze streamflow records of 7,053 catchments around the world over the period 1950–2016, and select 5,327 with reliable elasticity estimates for aggregation time ranging from 13 to 60 months. We find a significant scaling of streamflow elasticity to precipitation with aggregation time in 66% of the catchments which is much larger than expected by chance. Positive scaling occurs much more frequently than negative scaling. More arid/less rainy catchments, less forested catchments and catchments with a large base flow contribution to streamflow are more frequently characterized by a positive scaling. A random forest classification model identifies aridity index, latitude, mean annual precipitation, the potential evapotranspiration seasonality, the base flow index and the precipitation seasonality as relevant explanatory variables of the scaling. We interpret the sign of the scaling by non‐linear runoff generation in arid regions, by the effect of climate modes and snow processes, and by the regulation capacity of vegetation to transpire more water if the past years were wet. It is suggested to use decadal elasticities instead of annual elasticities in climate impact analyses in order to account for their scaling behavior.

Climate assessment of single flights: Deduction of route specific equivalent CO2 emissions

Climate impact of anthropogenic activities is more and more of public concern. But while CO2 emissions are accounted in emissions trading and mitigation plans, emissions of non-CO2 components contributing to climate change receive much less attention. One of the anthropogenic emission sectors, where non-CO2 effects play an important part, is aviation. Hence, for a quantitative estimate of total aviation climate impact, assessments need to comprise both CO2 and non-CO2 effects (e.g., water vapor, nitrogen dioxide, and contrails), instead of calculating and providing only CO2 impacts. However, while a calculation of CO2 effects relies directly on fuel consumption, for non-CO2 effects detailed information on aircraft trajectory, engine emissions, and ambient atmospheric conditions are required. As often such comprehensive information is not available for all aircraft movements, a simplified calculation method is required to calculate non-CO2 impacts. In our study, we introduce a simple calculation method which allows quantifying climate assessment relying on mission parameters, involving distance and geographic flight region. We present a systematic analysis of simulated climate impact from more than 1000 city pairs with an Airbus A330-200 aircraft depending on the flight distance and flight region to derive simplified but still realistic representation of the non-CO2 climate effects. These new formulas much better represent the climate impact of non-CO2 effects compared to a constant CO2 multiplier. The mean square error decrease from 1.18 for a constant factor down to 0.24 for distance dependent factors and can be reduced even further to 0.19 for a distance and latitude dependent factor.