A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

Projecting future precipitation change across the semi-arid Borana lowland,southern Ethiopia

Climate change caused by past,current,and future greenhouse gas emissions has become a major concern for scientists in the field in many countries and regions of the world.This study modelled future precipitation change by downscaling a set of large-scale climate predictor variables(predictors)from the second generation Canadian Earth System Model(CanESM2)under two Representative Concentration Pathway(RCP)emission scenarios(RCP4.5 and RCP8.5)in the semi-arid Borana lowland,southern Ethiopia.The Statistical DownScaling Model(SDSM)4.2.9 was employed to downscale and project future precipitation change in the middle(2036-2065;2050s)and far(2066-2095;2080s)future at the local scale.Historical precipitation observations from eight meteorological stations stretching from 1981 to 1995 and 1996 to 2005 were used for the model calibration and validation,respectively,and the time period of 1981-2018 was considered and used as the baseline period to analyze future precipitation change.The results revealed that the surface-specific humidity and the geopotential height at 500 hPa were the preferred large-scale predictors.Compared to the middle future(2050s),precipitation showed a much greater increase in the far future(2080s)under both RCP4.5 and RCP8.5 scenarios at all meteorological stations(except Teletele and Dillo stations).At Teltele station,the projected annual precipitation will decrease by 26.53%(2050s)and 39.45%(2080s)under RCP4.5 scenario,and 34.99%(2050s)and 60.62%(2080s)under RCP8.5 scenario.Seasonally,the main rainy period would shift from spring(March to May)to autumn(September to November)at Dehas,Dire,Moyale,and Teltele stations,but for Arero and Yabelo stations,spring would consistently receive more precipitation than autumn.It can be concluded that future precipitation in the semi-arid Borana lowland is predicted to differ under the two climate scenarios(RCP4.5 and RCP8.5),showing an increasing trend at most meteorological stations.This information could be helpful for policymakers to design adaptation plans in water resources management,and we suggest that the government should give more attention to improve early warning systems in drought-prone areas by providing dependable climate forecast information as early as possible.

Trend in Observed and Projected Maximum and Minimum Temperature over N-W Himalayan Basin

Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of water resource management. Future interannual and inter-seasonal variations in maximum and minimum temperature may bring significant changes in hydrological systems and affect regional water resources. The present study has been performed to observe past(1970-2010) as well as future(2011-2100)spatial and temporal variability in temperature(maximum and minimum) over selected stations of Sutlej basin located in North-Western Himalayan region in India. The generation of future time series of temperature data at different stations is done using statistical downscaling technique. The nonparametric test methods, modified Mann-Kendall test and Cumulative Sum chart are used for detecting monotonic trend and sequential shift in time series of maximum and minimum temperature. Sen's slope estimator test is used to detect the magnitude of change over a period of time on annual and seasonal basis. The cooling experienced in annual TMax and TMin at Kasol in past(1970-2010) would be replaced by warming in future as increasing trends are detected in TMax during 2020 s and 2050 s and in TMin during 2020 s, 2050 s and 2080 s under A1 B and A2 scenarios. Similar results of warming are also predicted at Sunnifor annual TMin in future under both scenarios which witnessed cooling during 1970-2010. The rise in TMin at Rampur is predicted to be continued in future as increasing trends are obtained under both the scenarios. Seasonal trend analysis reveals large variability in trends of TMax and TMin over these stations for the future periods.

Assessment of Future Climate Change Scenario in Halaba District, Southern Ethiopia

Climate change is one environmental threat that poses great challenges to the future development prospects of Ethiopia. The study used the statistically downscaled daily data in 30-years intervals from the second generation of the Earth System Model (CanESM2) under two Representative Concentration Pathways (RCPs): RCP 4.5 and RCP 8.5 for three future time slices;near-term (2010-2039), mid-century (2040-2069) and end-century (2071-2099) were generated. The observed data of maximum and minimum temperature and precipitation are a good simulation with the modeled data during the calibration and validation periods using the correlation coefficient (R2), the Nash-Sutcliffe efficiency (NSE), and the Root Mean Square Error (RMSE). The projected annual minimum and maximum temperatures are expected to increase by 0.091°C, 0.517°C, and 0.73°C and 0.072°C, 0.245°C, and 0.358°C in the 2020s, 2050s, and 2080s under the intermediate scenario, respectively. Under RCP8.5, the annual minimum and maximum temperatures are expected to increase by 0.192°C, 0.409°C, and 0.708°C, 0.402°C, 4.352°C, and 8.750°C in the 2020s, 2050s, and 2080s, respectively. Besides, the precipitation is expected to increase under intermediate and high emission scenarios by 1.314%, 7.643%, and 12.239%, and 1.269%, 10.316% and 26.298% in the 2020s, 2050s, and 2080s, respectively. Temperature and precipitation are projected to increase in total amounts under all-time slices and emissions pathways. In both emission scenarios, the greatest changes in maximum temperature, minimum temperature, and precipitation are predicted by the end of the century. This implies climate smart actions in development policies and activities need to consider locally downscale expected climatic changes.

Impact of climate change on water resources in the Yarmouk River Basin of Jordan

Understanding the impact of climate change on water resources is important for developing regional adaptive water management strategies.This study investigated the impact of climate change on water resources in the Yarmouk River Basin(YRB)of Jordan by analyzing the historical trends and future projections of temperature,precipitation,and streamflow.Simple linear regression was used to analyze temperature and precipitation trends from 1989 to 2017 at Irbid,Mafraq,and Samar stations.The Statistical Downscaling Model(SDSM)was applied to predict changes in temperature and precipitation from 2018 to 2100 under three Representative Concentration Pathway(RCP)scenarios(i.e.,RCP2.6,RCP4.5,and RCP8.5),and the Soil and Water Assessment Tool(SWAT)was utilized to estimate their potential impact on streamflow at Addasiyia station.Analysis of data from 1989 to 2017 revealed that mean maximum and minimum temperatures increased at all stations,with average rises of 1.62°C and 1.39°C,respectively.The precipitation trends varied across all stations,showing a significant increase at Mafraq station,an insignificant increase at Irbid station,and an insignificant decrease at Samar station.Historical analysis of streamflow data revealed a decreasing trend with a slope of–0.168.Significant increases in both mean minimum and mean maximum temperatures across all stations suggested that evaporation is the dominant process within the basin,leading to reduced streamflow.Under the RCP scenarios,projections indicated that mean maximum temperatures will increase by 0.32°C to 1.52°C,while precipitation will decrease by 8.5% to 43.0% throughout the 21st century.Future streamflow projections indicated reductions in streamflow ranging from 8.7% to 84.8% over the same period.The mathematical model results showed a 39.4% reduction in streamflow by 2050,nearly double the SWAT model's estimate under RCP8.5 scenario.This research provides novel insights into the regional impact of climate change on water resources,emphasizing the urgent need to address these environmental challenges to ensure a sustainable water supply in Jordan.

Change in rainfall erosivity in the past and future due to climate change in the central part of India

Temporal change in rainfall erosivity varies due to the rainfall characteristic(amount,intensity,frequency,duration),which affects the conservation of soil and water.This study illustrates the variation of rainfall erosivity due to changing rainfall in the past and the future.The projected rainfall is generated by SDSM(Statistical DownScaling Model)after calibration and validation using two GCMs(general circulation model)data of HadCM3(A2 and B2 scenario)and CGCM3(A1B and A2 scenario).The selected study area is mainly a cultivable area with an agricultural based economy.This economy depends on rainfall and is located in a part of the Narmada river basin in central India.Nine rainfall locations are selected that are distributed throughout the study area and surrounding.The results indicate gradually increasing projected rainfall while the past rainfall has shown a declined pattern by Mann–Kendall test with statistical 95%confidence level.Rainfall erosivity has increased due to the projected increase in the future rainfall(2080 s)in comparison to the past.Rainfall erosivity varies from
32.91%to 24.12%in the 2020s,
18.82 to 75.48%in 2050 s and 20.95–202.40%in 2080s.The outputs of this paper can be helpful for the decision makers to manage the soil water conservation in this study area.