Simulation and Evaluation of Statistical Downscaling of Regional Daily Precipitation over North China Based on Self-Organizing Maps

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10−4 and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China.

Climate Change Projections of Temperature and Precipitation in the Eastern Black Sea Basin, Turkey by using Multivariate Adaptive Regression Splines Statistical Downscaling Method

Climate change has recently become one of the most important issue discussed by scientists around the world. The purpose of this study is to investigate the possible effects of climate change on temperature and precipitation variables, which are among the most major climate variables in terms of their environmental and economic impact for the rainiest region of Turkey. General circulation models (GCMs) under different emission scenarios are widely used in determining possible changes in the climate system. However, the coarse resolutions of these models are unsuitable for the climate impact/adaptation studies at basin scale. In this paper, the future monthly mean temperature and precipitation for 12 station in the Eastern Black Sea Basin, Turkey were projected for three periods 2030s (2021–2050), 2060s (2051–2080), and 2090s (2081–2100) from the three GCMs, namely CNRM-CM5.1, HadGEM2-ES, and MPI-ESM-MR, under RCP4.5 (optimistic) and RCP8.5 (pessimistic) scenarios using the multivariate adaptive regression splines (MARS) statistical downscaling method. The statistical downscaling models were set up between the 12 potential predictor, obtained from ERA-Interim reanalysis data set, and the local station data. For evaluating and the performance of the downscaling models four performance statistics namely root means square error, scatter index, mean absolute error, and the Nash Sutcliffe coefficient of efficiency, were used. Thereafter, the calibrated and validated models were applied to downscale future scenarios of the GCMs. The results obtained from the study proved that the downscale temperature and precipitation values correlated well with the observation values for the training (1981–2004) and test (2005–2010) periods. An average increase of 2.5 and 2.0°C is foreseen according to the optimistic scenario and an average increase of 3.5 and 3.0°C is foreseen according to the pessimistic scenario in the southern and northern parts of the basin, respectively. As to precipitation, a decrease is expected in the southern part of the basin but a significant increase is expected especially in spring at the stations located in the western and coastal parts of the basin.

Comparisons of Machine Learning Methods of Statistical Downscaling Method: Case Studies of Daily Climate Anomalies in Thailand

The climate change which is essential for daily life and especially agriculture has been forecasted by global climate models (GCMs) in the past few years. Statistical downscaling method (SD) has been used to improve the GCMs and enables the projection of local climate. Many pieces of research have studied climate change in case of individually seasonal temperature and precipitation for simulation; however, regional difference has not been included in the calculation. In this research, four fundamental SDs, linear regression (LR), Gaussian process (GP), support vector machine (SVM) and deep learning (DL), are studied for daily maximum temperature (TMAX), daily minimum temperature (TMIN), and precipitation (PRCP) based on the statistical relationship between the larger-scale climate predictors and predictands in Thailand. Additionally, the data sets of climate variables from over 45 weather stations overall in Thailand are used to calculate in this calculation. The statistical analysis of two performance criteria (correlation and root mean square error (RMSE)) shows that the DL provides the best performance for simulation. The TMAX and TMIN were calculated and gave a similar trend for all models. PRCP results found that in the North and South are adequate and poor performance due to high and low precipitation, respectively. We illustrate that DL is one of the suitable models for the climate change problem.

Empirical statistical downscaling with EPISODES in an Alpine territory

<p>EPISODES is an empirical statistical downscaling method which has been developed at the German national weather service, DWD (Kreienkamp et al. 2019). Its main aim is the downscaling of climate projections and climate predictions (seasonal to decadal) from global climate models (GCMs) to regional scale. A specific aim is to enhance ensembles based on dynamical downscaling and to improve robustness of deduced indices and statements.</p><p>The methodology involves two main steps, first, analogue downscaling in connection with linear regression and, second, a sort of weather generator. An important precondition is the availability of long-term observation data sets of high quality and resolution. The synthetic time-series resulting from EPISODES are multivariate and consistent in space and time. The data provide daily values for selected surface variables and can be delivered on grid or station representation. As such, they meet the main requirements for applications in climate impact research. Thanks to low computational needs, EPISODES can provide climate projections within short time. This enables early insights in the local effects of climate change as projected by GCMs and allows flexibility in the selection of ensembles.</p><p>While good results for EPISODES projections have already been achieved for Germany, the methodology needs to be adapted for the more complex terrain of the Alpine region. This is done in close collaboration of DWD and ZAMG (Austria). Among other tasks, the adaptions include a regionalization of the selection of relevant weather regimes, optimal fragmentation of the target region into climatic sub-zones and correction of precipitation class frequencies.</p><p>The presentation will refer to the progress of the adaption process. In doing so the quality of downscaled climate projections is shown for a test ensemble in comparison with existing projections of the Austrian ÖKS15 data set and EURO-CORDEX. </p><p>Reference: Kreienkamp, F., Paxian, A., Früh, B., Lorenz, P., Matulla, C.: Evaluation of the empirical–statistical downscaling method EPISODES. <em>Clim Dyn</em> <strong>52, </strong>991–1026 (2019). https://doi.org/10.1007/s00382-018-4276-2</p>

On the sensitivity of 21st century spring plant phenology projections to the choice of statistical downscaling method

<p>We examine several springtime plant phenology indices calculated from a set of statistically downscaled daily minimum and maximum temperature projections. Multiple statistical downscaling methods are used to refine daily temperature projections from multiple global climate models (GCMs) run with multiple radiative forcing scenarios. Focusing on the northeastern United States, the statistically downscaled temperature projections are input to a commonly used Extended Spring Indices (SI-x) model, yielding yearly estimates of phenological indices such as First Leaf Date (an early spring indicator), First Bloom Date (a late spring indicator), and the occurrence of Late False Springs (a year in which a hard freeze occurs after first bloom, when plants are vulnerable to damage from freezing conditions). The matrix of results allows one to analyze how projected spring phenological index differences arising from the choice of statistical downscaling method (i.e., the statistical downscaling uncertainty) compare with the magnitudes of variations across the different GCMs (climate model uncertainty) and radiative forcing pathways (scenario uncertainty). As expected, the onset of spring in the late 21<sup>st</sup> century projections, as measured by First Leaf and First Bloom Dates, typically shifts multiple weeks earlier in the year compared with the historical period. Those two start-of-spring indices can be thought of as being largely, but not entirely, dependent on an accumulation of heat since 1 January. In contrast, a Late False Spring occurs in large part due to a short-term weather event - namely if any single day after the First Bloom Date has a minimum temperature below -2.2C. Accordingly, spring phenological indices calculated from statistically downscaled climate projections can be influenced by how well the GCM’s historical simulation represents temperature variations on different time scales (diurnal temperature range, synoptic time-scale temperature variability, inter-annual temperature variations) as well as how a particular statistical refinement method (e.g., a delta change factor method, a quantile-based bias correction method, or a constructed analog method) combines information gleaned from both the GCM time series and the observation-based training data to generate the statistically refined daily maximum and minimum temperature time series. Though this study is limited in scope (northeastern United States region, a finite set of statistical downscaling methods and GCMs), we believe the general findings likely are illustrative and applicable to a wider range of mid-latitude locations where plant responses in spring are mostly temperature and day length driven.</p>

Uncovering the shortcomings of a weather typing based statistical downscaling method

In recent years many methods for statistical downscaling of the climate model outputs have been developed. Each statistical downscaling method (SDM) has strengths and limitations, but those are rarely evaluated. This paper proposes an approach to evaluate the skill of SDMs for the specific purpose of impact analysis in hydrology. The skill is evaluated by the verification of the general statistical downscaling assumptions, and by the perfect predictor experiment that includes hydrological impact analysis. The approach has been tested for an advanced weather typing based SDM and for impact analysis on river peak flows in a Belgian river catchment. Significant shortcomings of the selected SDM were uncovered such as biases in the frequency of weather types and non-stationarities in the extreme precipitation distribution per weather type. Such evaluation of SDMs becomes of use for future tailoring of SDM ensembles to end user needs.