Downscaling Model Research Articles

Downscaling long lead time daily rainfall ensemble forecasts through deep learning

Skilful and localised daily weather forecasts for upcoming seasons are desired by climate-sensitive sectors. Various General circulation models routinely provide such long lead time ensemble forecasts, also known as seasonal climate forecasts (SCF), but require downscaling techniques to enhance their skills from historical observations. Traditional downscaling techniques, like quantile mapping (QM), learn empirical relationships from pre-engineered predictors. Deep-learning-based downscaling techniques automatically generate and select predictors but almost all of them focus on simplified situations where low-resolution images match well with high-resolution ones, which is not the case in ensemble forecasts. To downscale ensemble rainfall forecasts, we take a two-step procedure. We first choose a suitable deep learning model, very deep super-resolution (VDSR), from several outstanding candidates, based on an ensemble forecast skill metric, continuous ranked probability score (CRPS). Secondly, via incorporating other climate variables as extra input, we develop and finalise a very deep statistical downscaling (VDSD) model based on CRPS. Both VDSR and VDSD are tested on downscaling 60 km rainfall forecasts from the Australian Community Climate and Earth-System Simulator Seasonal model version 1 (ACCESS-S1) to 12 km with lead times up to 217 days. Leave-one-year-out testing results illustrate that VDSD has normally higher forecast accuracy and skill, measured by mean absolute error and CRPS respectively, than VDSR and QM. VDSD substantially improves ACCESS-S1 raw forecasts but does not always outperform climatology, a benchmark for SCFs. Many more research efforts are required on downscaling and climate modelling for skilful SCFs.

A Comparison of the Statistical Downscaling and Long-Short-Term-Memory Artificial Neural Network Models for Long-Term Temperature and Precipitations Forecasting

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models’ output, to improve water management and long-term temperature and precipitations forecasting at local level.

Global monthly sectoral water use for 2010–2100 at 0.5° resolution across alternative futures

Water usage is closely linked with societal goals that are both local and global in scale, such as sustainable development and economic growth. It is therefore of value, particularly for long-term planning, to understand how future sectoral water usage could evolve on a global scale at fine resolution. Additionally, future water usage could be strongly shaped by global forces, such as socioeconomic and climate change, and the multi-sector dynamic interactions those forces create. We generate a novel global gridded monthly sectoral water withdrawal and consumption dataset at 0.5° resolution for 2010–2100 for a diverse range of 75 scenarios. The scenarios are harmonized with the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios to support its usage in studies evaluating the implications of uncertain human and earth system change for future global and regional dynamics. To generate the data, we couple the Global Change Analysis Model (GCAM) with a land use spatial downscaling model (Demeter), a global hydrologic framework (Xanthos), and a water withdrawal downscaling model (Tethys).

A two-stage downscaling hydrological modeling approach via convolutional conditional neural process and geostatistical bias correction

Hydrological modeling plays an indispensable role in many practical applications, such as hydrology, water resources assessment, and environmental survey. Downscaling approach is able to obtain high-resolution hydrological models according to a prior reference model. In this work, we propose a downscaling hydrological modeling approach based on convolutional conditional neural processes (ConvCNP) and a novel geostatistical bias correction, named ConvCNP-GBC. A two-stage downscaling modeling workflow is presented. In the model building stage, to reduce the training consumption of the ConvCNP, low-resolution conditioning data are used for training. In the model generating stage, the ConvCNP is used to reconstruct coarse-scale realizations based on upscaling sampled conditioning data and to generate high-resolution realizations by downscaling. Besides, a geostatistical bias correction strategy is presented to refine large-scale realizations with high-resolution conditioning data constraints. The experimental results confirm that ConvCNP-GBC can reproduce heterogeneous patterns by using coarse-scale conditioning data and characterize hydrological structures. The downscaled realizations also have a high spatial correspondence with the coarse-scale realizations, which provide fine subsurface heterogeneous structures. The proposed method can be easily extended to reservoir modeling, geophysical inversion, etc.

Establishing Climatic Change on Rainfall Trend, Variation and Change Point Pattern in Benin City, Nigeria

This study examined the effect of changing climate for thirty-six years (1982-2017) on 24-hourly annual maximum time series (AMS) rainfall data for Benin City. The results show the existence of a trend, variation in magnitude, and change point dates. Trend analysis was performed using Mann-Kendall (MK) test and Sen’s slope estimator (SSE) applied to obtain the trend magnitude. A statistically significant trend was found for Benin City at a 5% level of significance. The MK |Z| statistic varied from 2.0710 to 2.1550 for IMD and 2.0844 to 2.0995 for MCIMD downscaling models, which were both greater than the critical Z-value of 1.96. The SSE gave the magnitude of the trend variation rate due to climate change at 24-hour duration as 1.008 mm/year (10.08 mm/decade) and 1.1376 mm/year (11.376 mm/decade) for the IMD and MCIMD models, respectively. Also, the trend change point analysis was conducted using the distribution-free cumulative sum test (CUSUM) and the sequential Mann-Kendall test (SQMK) which has rainfall pattern definite change point date for Benin City as 2005 from where continued a positive trend which intensified through the year 2015. Thus, proving the existence of increasing changing climatic condition for the study area.

Assessing the climate and air quality effects of future aerosol mitigation in India using a global climate model combined with statistical downscaling

Abstract. We studied the potential of using machine learning to downscale global-scale climate model output towards ground station data. The aim was to simultaneously analyze both city-level air quality and regional- and global-scale radiative forcing values for anthropogenic aerosols. As the city-level air pollution values are typically underestimated in global-scale models, we used a machine learning approach to downscale fine particulate (PM2.5) concentrations towards measured values. We first simulated the global climate with the aerosol–climate model ECHAM-HAMMOZ and corrected the PM2.5 values for the Indian megacity New Delhi. The downscaling procedure clearly improved the seasonal variation in the model data. The seasonal trends were much better captured in the corrected PM2.5 than in original ECHAM-HAMMOZ PM2.5 when compared to the reference PM2.5 from the ground stations. However, short-term variations showed less extreme values with the downscaling approach. We applied the downscaling model also to simulations where the aerosol emissions were following two different future scenarios: one following the current legislation and one assuming currently maximum feasible emission reductions. The corrected PM2.5 concentrations for the year 2030 showed that mitigating anthropogenic aerosols improves local air quality in New Delhi, with organic carbon reductions contributing most to these improvements. In addition, aerosol emission mitigation also resulted in negative radiative forcing values over most of India. This was mainly due to reductions in absorbing black carbon emissions. For the two future emission scenarios modeled, the radiative forcing due to aerosol–radiation interactions over India was -0.09±0.26 and -0.53±0.31 W m−2, respectively, while the effective radiative forcing values were -2.1±4.6 and 0.06±3.39 W m−2, respectively. Although accompanied by relatively large uncertainties, the obtained results indicate that aerosol mitigation could bring a double benefit in India: better air quality and decreased warming of the local climate. Our results demonstrate that downscaling and bias correction allow more versatile utilization of global-scale climate models. With the help of downscaling, global climate models can be used in applications where one aims to analyze both global and regional effects of policies related to mitigating anthropogenic emissions.

Quantitative assessment methodology framework of the impact of global climate change on the aquatic habitat of warm-water fish species in rivers

Global climate change (GCC), with global warming as the main characteristic, has become a global issue widely concerned by people. GCC impacts the hydrological regime at the watershed scale and affects the hydrodynamic force and the habitat conditions of freshwater ecosystems at the river scale. The impact of GCC on water resources and the water cycle is a research hotspot. However, there are few studies on water environment ecology related to hydrology and the influence of changes in discharge and water temperature on warm-water fish habitats. This study proposes a quantitative assessment methodology framework for predicting and analyzing the impact of GCC on the warm-water fish habitat. This system integrates GCC, downscaling, hydrological, hydrodynamic, water temperature and habitat models and was applied to the middle and lower reaches of the Hanjiang River (MLHR), where there are four major Chinese carps resource reduction problems. The results showed that the calibration and validation of the statistical downscaling model (SDSM) and the hydrological, hydrodynamic, and water temperature models were carried out using the observed meteorological factors, discharge, water level, flow velocity and water temperature data. The change rule of the simulated value was in good agreement with the observed value, and the models and methods used in the quantitative assessment methodology framework were applicable and accurate. The rise of water temperature caused by GCC will ease the problem of low-temperature water in the MLHR, and the weighted usable area (WUA) for spawning of the four major Chinese carps will appear in advance. Meanwhile, the increase in future annual discharge will play a positive role in WUA. In general, the rise in confluence discharge and water temperature caused by GCC will increase WUA, which is beneficial to the spawning ground of four major Chinese carps.

Time- and frequency-domain vibration analysis of enhanced pyramid lattice sandwich plates using an equivalent downscaling model

Time- and frequency-domain vibration analysis of enhanced pyramid lattice sandwich plates using an equivalent downscaling model

A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information

Precipitation data with high accuracy and spatial resolution characteristics play significant roles in the regional hydrological and eco-environmental system applications. Thus, satellite-based precipitation products (SPPs) with high spatial resolution and high accuracy must be developed. This study proposed a multiple-step scheme to improve the global precipitation measurement (GPM) at daily and monthly timescales over the Tibetan Plateau (TP) from 2014 to 2017. First, combined with the geographically weighted regression (GWR) method using geographic and topographic factors, the gamma-distribution mapping and local intensity scaling (GDM-LOCI) method is applied to effectively merge the observed data attributes and the spatial representation of SPPs at the daily scale by correcting precipitation volumes and frequencies. Second, the areal merged precipitation, normalized difference vegetation index (NDVI), and reanalyzed atmospheric data are used to improve the spatial resolution of monthly GPM with a random forest (RF) model that uses the 17 land cover types to establish the local downscaling model windows. The results show that daily merged precipitation can better reflect the spatial and temporal variability of precipitation than can satellite estimates, and the correlation coefficient (R), and critical success index (CSI) increased by 0.12 and 0.26, respectively. In the merged downscaling model, the merged precipitation factor can weaken the negative effect of the other auxiliary predictors due to its spatial autocorrelation in precipitation estimation. Most importantly, by using the land cover types to establish local model windows for the downscaling model, not only the spatial resolution of the GPM product is downscaled to 1 km, but also the spatial structure of the downscaled products is enhanced, with less deviation and a higher spatial correlation. The R, the root mean square error (RMSE) and the relative bias (BIAS) were 0.89, 50.19 mm and 0.57, respectively. This study presents a promising scheme for generating high-quality precipitation data for regional hydrometeorological research in data-scarce regions.

Potential Impacts of Climate Change on Surface Water Resources in Arid Regions Using Downscaled Regional Circulation Model and Soil Water Assessment Tool, a Case Study of Amman-Zerqa Basin, Jordan

Water scarcity, aggravated by climate change impacts, threatens all sectors in arid regions and hampers sustainable development plans. This work aims to assess the potential impacts of climate change on surface water resources of Amman-Zerqa Basin, Jordan, using the Soil Water Assessment Tool model (SWAT) and outputs from the Downscaled Regional Circulation Model. Future scenarios were developed based on combining two Representative Concentration Pathways (RCPs 4.5 and 8.5). A reference scenario from 1973 to 2015 was used to compare the current climate with future climates and their impacts on hydrological processes. Hydrologic modeling outputs showed very good performance ratings for calibration and validation periods. Statistical bias correction of the Downscale Regional Circulation Model (GCM) indicated that linear scaling for precipitation data was the best-performing bias correction method, along with variance scaling and distribution mapping methods for minimum and maximum temperature, respectively. The coupled future model simulations indicated a reduction in crucial water balance components under all modeled scenarios. The simulated reductions range between 3.7% and 20.7% for precipitation, 22.3–41.6% for stream flow, 25.0–47.0% for surface runoff, 0.5–13.4% for evapotranspiration, and 21.5–41.4% for water yield, from conservative to the severe scenario, respectively. In conclusion, spatial analyses indicated the presence of three zones of impact. Thus, future climate and hydrological adaptation measures should focus on the provided zoning.

The meso scale as a frontier in interdisciplinary modeling of sustainability from local to global scales

Achieving sustainable development requires understanding how human behavior and the environment interact across spatial scales. In particular, knowing how to manage tradeoffs between the environment and the economy, or between one spatial scale and another, necessitates a modeling approach that allows these different components to interact. Existing integrated local and global analyses provide key insights, but often fail to capture ‘meso-scale’ phenomena that operate at scales between the local and the global, leading to erroneous predictions and a constrained scope of analysis. Meso-scale phenomena are difficult to model because of their complexity and computational challenges, where adding additional scales can increase model run-time exponentially. These additions, however, are necessary to make models that include sufficient detail for policy-makers to assess tradeoffs. Here, we synthesize research that explicitly includes meso-scale phenomena and assess where further efforts might be fruitful in improving our predictions and expanding the scope of questions that sustainability science can answer. We emphasize five categories of models relevant to sustainability science, including biophysical models, integrated assessment models, land-use change models, earth-economy models and spatial downscaling models. We outline the technical and methodological challenges present in these areas of research and discuss seven directions for future research that will improve coverage of meso-scale effects. Additionally, we provide a specific worked example that shows the challenges present, and possible solutions, for modeling meso-scale phenomena in integrated earth-economy models.

Projections and impact assessment of the local climate change conditions of the Black Volta Basin of Ghana based on the Statistical DownScaling Model

Abstract The uncertainties and biases associated with Global Climate Models (GCMs) ascend from global to regional and local scales which delimits the applicability and suitability of GCMs in site-specific impact assessment research. The study downscaled two GCMs to evaluate effects of climate change (CC) in the Black Volta Basin (BVB) using Statistical DownScaling Model (SDSM) and 40-year ground station data. The study employed Taylor diagrams, dimensionless, dimensioned, and goodness of fit statistics to evaluate model performance. SDSM produced good performance in downscaling daily precipitation, maximum and minimum temperature in the basin. Future projections of precipitation by HadCM3 and CanESM2 indicated decreasing trend as revealed by the delta statistics and ITA plots. Both models projected near- to far-future increases in temperature and decreases in precipitation by 2.05-23.89, 5.41–46.35, and 5.84–35.33% in the near, mid, and far future respectively. Therefore, BVB is expected to become hotter and drier by 2100. As such, climate actions to combat detrimental effects on the BVB must be revamped since the basin hosts one of the largest hydropower dams in Ghana. The study is expected to support the integration of CC mitigation into local, national, and international policies, and support knowledge and capacity building to meet CC challenges.

Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia

The objective of this study is to investigate and perform long–term forecasting of both streamflow and hydrological drought over Ethiopia. Observed streamflow and precipitation data are collected from 17 streamflow stations and 34 rainfall gauge stations to forecast future streamflow and hydrological drought from 2026 to 2099. Streamflow forecasting is performed using an artificial neural network (ANN) in conjunction with python software. Observed precipitation and streamflow data from 1973 to 2014 are used to train and test the ANN model by 70 and 30% ratios, respectively. After training the model, future downscaled precipitation data from regional climate models (RCM) have been used as input data to forecast future streamflow. Three RCM models were used to downscale historical and future climate data. RACMO is found a good downscaling model for all selected stations. The linear scaling bias correction technique results in less than 2% error compared to other alternative techniques. The result indicates that ANN is a good tool to forecast streamflow in areas having a good correlation between precipitation and streamflow such as Abbay, Awash, Baro, Omo Gibe, and Tekeze river basins. But in arid areas for example Genale Dawa, Wabishebele, and Rift Valley basins, the model is not suitable because the input data (precipitation) have high variation than the output variable (streamflow). In such areas, meteorological drought analysis and forecasting are better than hydrological drought analysis. Finally, future hydrological drought is analyzed using forecasted streamflow data as input to the streamflow drought index (SDI). The result indicates that 2028, 2036, 2042, 2044, 2062, and 2063 are the expected extreme drought years in most river basins of Ethiopia in the future. This shows that at least one extreme drought is expected in each decade in the future. Therefore, extensive research in drought analysis and forecasting is needed to develop an effective drought early warning system, and water resource management policy.

Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection

Central Asia (CA) is highly sensitive and vulnerable to changes in precipitation due to global warming, so the projection of precipitation extremes is essential for local climate risk assessment. However, global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes, especially in areas with complex terrain. In this study, we proposed a statistical downscaling (SD) model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models (GCMs) for present and future (2081–2100) periods under two shared socioeconomic pathways (SSP245 and SSP585). The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity (SDII) and maximum 1-day precipitation (RX1DAY) and overestimate the number of wet days (R1MM) and maximum consecutive wet days (CWD) at stations across CA. However, the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations. Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA. In addition, it is skilled in capturing the spatial patterns of the observed precipitation indices. Obviously, SDII and RX1DAY are improved by the SD model, especially in the southeastern mountainous area. Under the intermediate scenario (SSP245), our SD multi-model ensemble projections project significant and robust increases in SDII and total extreme precipitation (R95PTOT) of 0.5 mm d−1 and 19.7 mm, respectively, over CA at the end of the 21st century (2081–2100) compared to the present values (1995–2014). More pronounced increases in indices R95PTOT, SDII, number of very wet days (R10MM), and RX1DAY are projected under the higher emission scenario (SSP585), particularly in the mountainous southeastern region. The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081–2100. The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA.

Development of statistical downscaling model based on Volterra series realization, principal components and ridge regression

AbstractImpacts of the global climate change in hydrology and water resources are accessed by downscaling of local daily rainfall from large-scale climate variables. This study developed a statistical downscaling model based on the Volterra series, principal components and ridge regression. This model is known, hereafter as SDCRR. The proposed model is applied at four different stations of the Manawatu River basin, in the North Island of New Zealand to downscale daily rainfall. The large-scale climate variables from the National Centers for Environmental Predictions (NCEP) reanalysis data are used in the present study to obtain with the wide range (WR) and the restricted range (RR) of predictors. The developed SDCRR model incorporated the climate change signals sufficiently by working with WR predictors. Further, principal component analysis (PC) was applied to the set of WR predictors, which were also used as the orthogonal filter in the ridge regression model to deal with the multi-collinearity. The ridge regression coefficients determined were less sensitive to random errors, and were capable of reducing the mean square error between the observed and the simulated daily precipitation data. Thus, the combined application of principal component analysis (PCA) and ridge regression improved the performance of the model. This combination is steady enough to capture appropriate information from predictors of the region. The performance of the SDCRR model is compared with that of the widely used statistical downscaling model (SDSM). The results of the study show the SDCRR model has better performance than the SDSM.

Stepwise cluster ensemble downscaling for drought projection under climate change

AbstractDrought is one of the most serious natural disasters exacerbated by climate change. Changes in precipitation and temperature in the future increase the likelihood of drought in China. In this study, a stepwise cluster ensemble downscaling (SCED) model was developed to bias‐correct projections of temperature and precipitation from multiple RCM outputs, and further characterized the drought hazards. The developed SCED model was used to aggregate and correct the results of multiple regional climate models, and its performance was proved to be reliable by comparing with the observed results. The proposed SCED method has been applied for drought projections over the Fujian province, China. The results showed that the changes of precipitation and temperature in Fujian would have obvious spatial heterogeneous characteristics. The temperature in the southeast coastal areas will increase by up to 4°C and the precipitation will decrease by 3.1% in the late 21st century, while the temperature rises and precipitation increases in the southwest. Temperature in inland areas will be lower and precipitation will be less. The drought hazards were also characterized by both SPI and SPEI based on biased‐corrected projections from SCED model. According to the SPI and SPEI indices, although the number of dry months in Fujian province will not change significantly in future, the spatial and temporal heterogeneity may become more explicit. Moreover, the moderate drought (from SPI) may increase while the general drought may decrease (from both SPI and SPEI). For extreme droughts, there would not be visible changes detected by SPI, but an increasing trend characterized since the impact of temperature was included in SPEI. In addition, there would be an increasing trend on drought when increasing temperature and precipitation occurred simultaneously.

Long-term wave energy potential estimation in the Aegean and Ionian seas using dynamic downscaling and wave modelling techniques.

The current work focuses on the use of dynamic downscaling methods based on 40 years ERA5 hindcast climatic data, for estimating the Wave energy potential for the Hellenic region. The main areas of interest are the Aegean and Ionian islands. The combination of local and reanalysis climatic data, has been used, as it is linking the mesoscale meteorology, with microclimatic weather phenomena. This way the driving geostrophic winds are effectively taken into account for the estimation of low altitude wind distribution. WRF is used to downscale the raw reanalysis ERA5 input data grid to a finer 3 × 3 Km results grid. This way, high computational accuracy can be achieved over the investigated regions, thus revealing finer wind scales phenomena. The downscaled climatic data are used as initial and boundary conditions for NOAA/NCEP's WAVEWATCH III to produce the required wave climate for the areas of interest over a finer grid. The computational results are compared with existing measurements and the significant wave height, wave period and direction distributions are used to assess in detail the potential wave energy distribution in specific areas of interest. The seasonal wave-climate variability and long-term periodicity are also investigated.

Agricultural water allocation with climate change based on gray wolf optimization in a semi-arid region of China.

We quantified and evaluated the allocation of soil and water resources in the Aksu River Basin to measure the consequences of climate change on an agricultural irrigation system. We first simulated future climate scenarios in the Aksu River Basin by using a statistical downscaling model (SDSM). We then formulated the optimal allocation scheme of agricultural water as a multiobjective optimization problem and obtained the Pareto optimal solution using the multi-objective grey wolf optimizer (MOGWO). Finally, optimal allocations of water and land resources in the basin at different times were obtained using an analytic hierarchy process (AHP). (1) The SDSM is able to simulate future climate change scenarios in the Aksu River Basin. Evapotranspiration (ET0) will increase significantly with variation as will the amount of available water albeit slightly. (2) To alleviate water pressure, the area of cropland should be reduced by 127.5 km2 under RCP4.5 and 377.2 km2 under RCP8.5 scenarios. (3) To be sustainable, the allocation ratio of forest land and water body should increase to 39% of the total water resource in the Aksu River Basin by 2050.

Projecting Future Energy Production from Operating Wind Farms in North America. Part II: Statistical Downscaling

Abstract Capacity factors (CFs) derived from daily expected power at 22 operating wind farms in different regions of North America are used as predictands to train statistical downscaling algorithms using output from ERA5. The statistical downscaling models are then used to make CF projections for a suite of CMIP6 Earth System Models (ESMs). Downscaling is performed using a hybrid statistical approach that employs synoptic types derived using k-means clustering applied to sea level pressure fields with variance corrections applied as a function of the pressure gradient intensity. ESMs exhibit marked variability in terms of the skill with which the frequency of synoptic types and pressure gradients are reproduced relative to ERA5, and that differential skill is used to infer differential credibility in the associated CF projections. Projections of median annual mean CF [P50(CF)] in each 20-yr period from 1980 to 2099 show evidence of declines at most wind farms except in parts of the southern Great Plains, although the magnitude of the changes is strongly dependent on the ESM. For example, P50(CF) in 2080–99 deviate from those in 1980–99 by from −3.1 to +0.2 percentage points in the Northeast. The largest-magnitude declines in P50(CF) ranging from −3.9 to −2 percentage points are projected for the southern West Coast. CF trends exhibit marked seasonality and are strongly linked to changes in the relative intensity of future synoptic patterns, with much less impact from shifts in the occurrence of synoptic types over time. Internal climate modes continue to play a significant role in inducing interannual variability in wind power production, even under high radiative forcing scenarios. Significance Statement We describe how future climate changes may affect wind resources and wind power generation. Near-term changes in projected wind power electricity generation potential at operating wind farms over North America are small, but by the end of the current century electricity production is projected to decrease in many areas but may increase in parts of the southern Great Plains. The amount of change in projected wind power production is a strong function of the Earth system model that is downscaled and also depends on the continued presence of internally forced climate variability. An additional dependence on the amount of greenhouse gas–induced global warming indicates the transition of the energy sector to low-carbon sources may assist in maintaining the abundant U.S. wind resource.

High resolution spatial-temporal downscaling model for historical daily precipitation using INLA

Precipitation and precipitation extremes have long been challenging to estimate. Significant progress has been made with successive generations of Earth system models capable of simulating our climate with a global coverage. In this paper, a new statistical approach is presented based on the integrated nested Laplace approximation (INLA) method to downscale historical daily precipitation rates. The spatial and spatio-temporal structures were used in a Bayesian approach, to produce a daily 5 km regular grid for continental Portugal. Results show the capability of the method to provide fast results aligned with the observations, but still underestimating precipitation events higher than 100 mm/day.