Country-resolved combined emission and socio-economic pathways based on the Representative Concentration Pathway (RCP) and Shared Socio-Economic Pathway (SSP) scenarios

Climate policy analysis needs reference scenarios to assess emission targets and current trends. When presenting their national climate policies, countries often showcase their target trajectories against fictitious so-called baselines. These counterfactual scenarios are meant to present future greenhouse gas (GHG) emissions in the absence of climate policy. These so-called baselines presented by countries are often of limited use, as they can be exaggerated and as the methodology used to derive them is usually not transparent. Scenarios created by independent modeling groups using integrated assessment models (IAMs) can provide different interpretations of several socio-economic storylines and can provide a more realistic backdrop against which the projected target emission trajectory can be assessed. However, the IAMs are limited in regional resolution. This resolution is further reduced in intercomparison studies, as data for a common set of regions are produced by aggregating the underlying smaller regions. Thus, the data are not readily available for country-specific policy analysis. This gap is closed by downscaling regional IAM scenarios to the country level. The last of such efforts has been performed for the SRES (“Special Report on Emissions Scenarios”) scenarios, which are over a decade old by now. CMIP6 (Coupled Model Intercomparison Project phase 6) scenarios have been downscaled to a grid; however they cover only a few combinations of forcing levels and SSP storylines with only a single model per combination. Here, we provide up-to-date country scenarios, downscaled from the full RCP (Representative Concentration Pathway) and SSP (Shared Socio-Economic Pathway) scenario databases, using results from the SSP GDP (gross domestic product) country model results as drivers for the downscaling process. The data are available at https://doi.org/10.5281/zenodo.3638137 (Gütschow et al., 2020).

Health Burden and Economic Impacts Attributed to PM2.5 and O3 in China from 2010 to 2050 under Different Representative Concentration Pathway Scenarios

Background: Quantifying future health burden attributed to fine particulate matters (PM2.5) and ozone (O3) in China is challenging when jointly accounting for emissions, climate and population changes. Future health burdens caused by PM2.5 and O3 in China remain largely understudied. Methods: In this paper, we used the Goddard Earth Observing System chemical transport model (GEOS-Chem) to project PM2.5 and O3 concentrations from 2010 to 2050 under four Representative Concentration Pathway scenarios (RCPs), then projected the PM2.5 and O3-related premature mortality and years of life lost (YLL) in this period. We then estimated the resulting economic burdens such as medical expenses (ME) and value of statistical life (VSL) in 2010-2050 attributed to the burdens of disease on PM2.5 and O3.Results: Compared to the targeted year 2050, we found that PM2.5 concentrations changed between -31.5% to 14.5% since 2010, resulted in -13.5% to 9.4% change in PM2.5-related mortality and -25.7% to 0.6% change in YLL across all the RCPs scenarios. For O3, the concentrations varied -13.3% to 3.7% by 2050, contributing to -26.9% to 13.1% change in O3-related mortality and -48.8% to 4.0% change in YLL. The lowest health impacts occurred in the RCP4.5 scenario by 2050 for both pollutants. In 2010, the ME caused by PM2.5 and O3 was $6.3-6.5 billion, and the VSL was $112.1-114.9 billion, accounting for 2.9-3.0% of the total GDP ($3874 billion). By 2050, ME and VSL will change from -19.7% to 17.5% and from -65.5% to 136.6%, respectively.Conclusion: This study suggested that future PM2.5 and O3 under certain RCP scenarios can have large health and economic benefits. However, given that the future population will always be higher than the baseline in 2010, more aggressive air pollution mitigation measures are needed for China.

Intercomparison of Bias-Correction Data Sources and Their Influence on Watershed-Specific Downscaling Climate Projections

HighlightsBias correction from three historical data sources (NCDC, PRISM, and NEXRAD) were assessed.Bias correction removed watershed-average bias in general circulation model (GCM) data for a historical period.Subbasin-specific variability was detected in future projections for each bias-correction data source.Data source had less impact on uncertainty in future projections than GCM or a representative concentration pathway.Uncertainty from bias-correction data sources was higher for precipitation than for temperature.Abstract. Climate projections developed by general circulation models (GCM) are often used in watershed modeling applications to project future hydrologic changes. In many models, the climate projections are downscaled to individual map units represented by grid cells or subbasins. Uncertainty of downscaled climate projections are a product of uncertainties arising mainly from the model itself, from the representative concentration pathway (RCP), and from the downscaling procedure. Other sources of uncertainty may include the historical observations used for GCM bias correction and data aggregation from GCM grids to map (often subbasin) units. This study evaluated effects of three sources of historical data (ground-based weather station network, NCDC, and two gridded datasets, NEXRAD and PRISM) on historical variability, and shifts and uncertainty in precipitation and temperature projections. Climate projections from six GCMs and three RCPs were evaluated in 54 subbasins of the Smoky Hill River watershed in the U.S. Central Great Plains. Bias correction of GCM projections reduced bias of watershed-average annual precipitation in the historical period to near zero, but subbasin-specific variability remained in future projections with little difference among bias-correction data sources. For minimum and maximum temperatures, the GCM ensemble statistics for basin-average and subbasin-specific future projections were similar for all bias-correction data sources. Increase in RCP forcing was found to widen the uncertainty in future projections. Overall, the uncertainty due to data source selection was smaller than the uncertainty due to GCM model and RCP forcing selection. The results demonstrate that statistical downscaling is essential to account for local climate factors within a watershed, and that both weather station-based and gridded bias-correction data sources can be used effectively, but that future climate projections may inherit the historical bias in a selected data source. These inherent uncertainties associated with application of GCMs in hydrological and geospatial modeling should be carefully considered for understanding climate projections when building watershed models and interpreting the results. Keywords: Bias correction, Climate change, Downscaling, GCM, Uncertainty, Watershed.

Change in Stream Flow of Gumara Watershed, upper Blue Nile Basin, Ethiopia under Representative Concentration Pathway Climate Change Scenarios

Climate change plays a pivotal role in the hydrological dynamics of tributaries in the upper Blue Nile basin. The understanding of the change in climate and its impact on water resource is of paramount importance to sustainable water resources management. This study was designed to reveal the extent to which the climate is being changed and its impacts on stream flow of the Gumara watershed under the Representative Concentration Pathway (RCP) climate change scenarios. The study considered the RCP 2.6, RCP 4.5, and RCP 8.5 scenarios using the second-generation Canadian Earth System Model (CanESM2). The Statistical Downscaling Model (SDSM) was used for calibration and projection of future climatic data of the study area. Soil and Water Assessment Tool (SWAT) model was used for simulation of the future stream flow of the watershed. Results showed that the average temperature will be increasing by 0.84 °C, 2.6 °C, and 4.1 °C in the end of this century under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios, respectively. The change in monthly rainfall amount showed a fluctuating trend in all scenarios but the overall annual rainfall amount is projected to increase by 8.6%, 5.2%, and 7.3% in RCP 2.6, RCP 4.5, and RCP 8.5, respectively. The change in stream flow of Gumara watershed under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios showed increasing trend in monthly average values in some months and years, but a decreasing trend was also observed in some years of the studied period. Overall, this study revealed that, due to climate change, the stream flow of the watershed is found to be increasing by 4.06%, 3.26%, and 3.67%under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios, respectively.

Short-Term Responses of Air Quality to Changes in Emissions under the Representative Concentration Pathway 4.5 Scenario over Brazil

Brazil, one of the world’s fastest-growing economies, is the fifth most populous country and is experiencing accelerated urbanization. This combination of factors causes an increase in urban population that is exposed to poor air quality, leading to public health burdens. In this work, the Weather Research and Forecasting Model with Chemistry is applied to simulate air quality over Brazil for a short time period under three future emission scenarios, including current legislation (CLE), mitigation scenario (MIT), and maximum feasible reduction (MFR) under the Representative Concentration Pathway 4.5 (RCP4.5), which is a climate change scenario under which radiative forcing of greenhouse gases (GHGs) reach 4.5 W m−2 by 2100. The main objective of this study is to determine the sensitivity of the concentrations of ozone (O3) and particulate matter with aerodynamic diameter 2.5 µm or less (PM2.5) to changes in emissions under these emission scenarios and to determine the signal and spatial patterns of these changes for Brazil. The model is evaluated with observations and shows reasonably good agreement. The MFR scenario leads to a reduction of 3% and 75% for O3 and PM2.5 respectively, considering the average of grid cells within Brazil, whereas the CLE scenario leads to an increase of 1% and 11% for O3 and PM2.5 respectively, concentrated near urban centers. These results indicate that of the three emission control scenarios, the CLE leads to poor air quality, while the MFR scenario leads to the maximum improvement in air quality. To the best of our knowledge, this work is the first to investigate the responses of air quality to changes in emissions under these emission scenarios for Brazil. The results shed light on the linkage between changes of emissions and air quality.

BALANÇO HÍDRICO CLIMATOLÓGICO PARA ITAJUBÁ-MG: CENÁRIO ATUAL E PROJEÇÕES CLIMÁTICAS

Projeções climáticas ao longo do século XXI têm indicado aumento da temperatura do ar, e irregularidades na precipitação no Estado de Minas Gerais (MG), os quais afetarão a evapotranspiração e, consequentemente, o balanço hídrico. Dessa forma, o objetivo desse trabalho foi verificar o impacto das mudanças climáticas no balanço hídrico climatológico (BHC) no município de Itajubá no período de 2021 a 2099 considerando o cenário de emissão Representative Concentration Pathway (RCP) 8.5 do Intergovernmental Panel on Climate Change (IPCC). Neste estudo, foram utilizadas projeções do modelo climático regional RegCM4 dirigido pelas saídas do modelo global HadGEM2-ES. O BHC foi calculado para o clima presente (1979 – 2005), futuro próximo (2021 – 2049) e futuro distante (2071 – 2099), considerando o método de Thornthwaite e Mather simplificado, em que considera como dado de entrada a climatologia mensal da temperatura média do ar e da precipitação. Projeta-se, no final do século XXI, um aumento de aproximadamente 5ºC na temperatura média do ar para Itajubá, além de um padrão heterogêneo de precipitação, com projeções de aumento nos meses de primavera e verão e redução nos meses de outono e inverno. Para o BHC é projetado aumento gradual na evapotranspiração real, chegando a 86 mm mês-1 nos meses de verão, e alteração na deficiência (DEF) e excedente hídrico (EXC). De maneira geral, é projetado um aumento da DEF e EXC nos meses de outono, inverno e verão, respectivamente. Tais alterações podem acarretar prejuízos para os principais cultivos de Itajubá, como o milho, a batata, o feijão e o café.