Climate zones of Turkey are redefined by using the mathematical methodology of cluster analysis. Data from 113 climate stations for temperatures (mean, maximum and minimum) and total precipitation from 1951 to 1998 are used after standardizing with zero mean and unit variance, to confirm that all variables are weighted equally in the cluster analysis. Hierarchical cluster analysis is chosen to perform the regionalization. Five different techniques were applied initially to decide the most suitable method for the region. Stability of the clusters is also tested. It is decided that Ward's method is the most likely to yield acceptable results in this particular case, as is often the case in climatological research. Seven different climate zones are found, as in conventional climate zones, but with considerable differences at the boundaries. Copyright © 2003 Royal Meteorological Society

In the present study, annual, wet and dry seasons precipitation records for the period 1961–2008 from 271 stations in Turkey were analysed using the rotated empirical orthogonal function (REOF), the Mann‐Kendall trend test and the continuous wavelet transform (WT) method. Additionally, relationships between time variability of the significant spatial patterns and North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and North‐Sea Caspian Pattern (NCP) are examined. The REOF method was used to analyse the annual, dry, and wet season variability of precipitation patterns over Turkey. The Mann‐Kendall method was used to detect the temporal trend of the rotated principal components (RPCs) time series, and the continuous wavelet method was used to explore the periodicity of precipitation changes. Continuous WT results indicate that the significant 3–4 year, 6–10 year, and 12–16 year bands are the major period components. Precipitation in Turkey is uneven in space and time, and its complex temporal structure and spatial variations are different in each dry and wet season. The Mann‐Kendall test results show that decreasing annual precipitation is the dominating trend throughout Anatolia, including west, and southwest sections. Increasing annual precipitation can be observed in only northeast Black Sea region of Turkey. Decreasing wet/dry season precipitation that we observe throughout the country, except northeast coasts and eastern parts of Turkey, is expected to have a strong impact on the economic livelihood of the region, especially on agricultural production, drinking water supply, and hydroelectricity production. Copyright © 2010 Royal Meteorological Society

Wind energy is a rapidly growing industry in Turkey. Wind power potential studies revealed that the most promising region for electricity generation is the western part of Turkey. Wind speed forecasting is necessary for power systems because of the intermittent nature of wind. Thus, accurate forecasting of wind power is recognized as a major contribution to reliable wind power integration. This paper assesses the performance of the weather research forecasting (WRF) model for wind speed and wind direction predictions up to 72 h ahead. The wind speeds and wind directions are evaluated based on the mean absolute error (MAE). Evaluations were also performed seasonally. Moreover, in order to improve the WRF simulations, a multi-input–single output artificial neural network (ANN) approach is applied to both wind speeds of the WRF model and wind power estimates, which are estimated from the wind speeds of the WRF model by using a power curve for the Soma wind power plant. Traditional error metrics were used for validations using wind tower mast data installed nearby the wind farm. The results from up to 72 h forecast horizon show that the WRF model slightly overpredicts the wind speeds. Wind speed predictions by the WRF model are found highly depending on the season, location, and wind direction. The model is also able to reproduce wind directions except for low wind speeds. Large MAEs are found for the winds less than 5 m/s. The performance of the WRF model for wind power prediction decreases with the increasing runtime. Root mean square error and normalized root mean square error (nRMSE) in wind powers range in between 123–261 kW and 13%–32% without performing the ANN approach, respectively. The improvement of the ANN depends on the forecast horizon, season, and location of turbine groups, as well as its application on either the wind speed outputs of the WRF model or wind power estimations. The ANN significantly improves the WRF at large forecast horizons for wind power estimations, for which it gives better results in the summer and reaches 29% improvement for summer on average for nRMSE. On the other hand, ANN adjusts the wind speed outputs of the model better than that of wind power estimations. For instance, the nRMSE is approximately 13% for 24 h winter wind speed simulations of the WRF for the turbine groups G1 and G4, after ANN adjustment. The ANN improves the results better for turbine group 1, because of less complexity of this group in the direction of prevailing wind. The evaluation of the ANN suggests that the approach can be used for improving the performance of the wind power forecast for this power plant.

Global climate projections show that humid heat extremes will expand toward the higher latitudes, making the midlatitudes hotspots for these extremes. Therefore, a thorough explanation of their regional characteristics becomes crucial, given that the changes in these extremes can potentially render a large proportion of the global population at risk. Here, we perform the first analysis of historical and projected changes in the intensity and frequency of humid heat extremes and quantify the population exposure to these extremes in Turkey, using long‐term simulations from the non‐hydrostatic mesoscale model of Consortium for Small‐scale Modeling (COSMO‐CLM) under the RCP8.5 emission scenario. We portray not only the nationwide changes in the humid heat extremes and population exposure but also their regional aspects by exploiting the K‐means clustering algorithm. Our results suggest significant future increases in the intensity and frequency of these extremes over a wide geographical area, which includes the surroundings of Adana, Antalya, Izmir, Sakarya, Ordu and Diyarbakir, most of which are coastal locations. Over most of these regions, severe humid heat stress is expected to last nearly a month every year, with almost 56% of the land area is projected to experience local historical upper tail heat stress conditions for at least an additional 10 consecutive hours. Further, we explicate a significant rise in the number of people exposed to severe humid heat stress, concentrated along most coastal regions, by as much as 1.6 million person‐days. More than 20% of Turkey's population may confront severe humid heat stress for at least 1 h, with that percentage falling to 4.15% for at least five consecutive hours, which indicates that people will not only endure more intense humid heat stress but also be exposed to these conditions consecutively over a period of many hours.

The performance of the climate simulations by the regional climate model COSMO‐CLM forced by MPI‐ESM‐LR, as well as projected future temperature and precipitation conditions over northwestern Türkiye at the convection‐permitting resolution, are shown in this study. In terms of mean, maximum and minimum temperatures and daily total precipitation, the model response was examined. Comparisons with observations were made on an annual and seasonal basis. The convection‐permitting model (0.0275°) provides a satisfactory representation of annual and seasonal mean temperatures according to bias, MAE and RMSE. Better results were found for precipitation with respect to RE, MAE and RMSE. Finally, we used the RCP8.5 emission scenario to investigate future climate changes in terms of average temperature and precipitation variations for northwestern Türkiye, including Istanbul, over two projection periods (2041–2060 and 2071–2090) compared to the reference period (1991–2005). The model predicts significant warming in northwestern Türkiye, particularly in Istanbul and its environs, by the end of the century, as well as a general decrease in precipitation, especially evident in the spring and summer. The findings of this study can be used to develop climate adaptation policies based on temperature and precipitation variables in and around the study area.

This study focuses on simulating the impacts of wind farm wake due to changes in the Mellor-Yamanda-Nakanishi-Niino (MYNN) planetary boundary layer (PBL) scheme in a high-resolution mesoscale Weather Research and Forecasting (WRF) model for a non-flat region in Turkey. This is the first study with a comprehensive evaluation of simulated wind farm wake impact responses to changes in the MYNN PBL scheme in the WRF model. Our results show that the WRF-WFP solutions for the wind farm wake impact significantly change with a change in the MYNN PBL scheme. In addition, the incorrect TKE advection and the correction factor of 0.25 for the TKE coefficient in wind farm parametrization (WFP) cause incorrect wind farm wake impacts especially on TKE and air temperature. Our study also shows that modifications in the mixing length create greater changes in simulated wind farm wake impacts than activation of the mass-flux scheme. In this study, the relative contributions of WFP’s components are also evaluated.