Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed

Early warning information on crop yield and production are very crucial for both farmers and decision-makers. In this study, we assess the skill and the reliability of the Integrated Canadian Crop Yield Forecaster (ICCYF), a regional crop yield forecasting tool, at different temporal (i.e. 1–3 months before harvest) and spatial (i.e. census agricultural region – CAR, provincial and national) scales across Canada. A distinct feature of the ICCYF is that it generates in-season yield forecasts well before the end of the growing season and provides a probability distribution of the forecasted yields. The ICCYF integrates climate, remote sensing derived vegetation indices, soil and crop information through a physical process-based soil water budget model and statistical algorithms. The model was evaluated against yield survey data of spring wheat, barley and canola during the 1987–2012 period. Our results showed that the ICCYF performance exhibited a strong spatial pattern at both CAR and provincial scales. Model performance was better from regions with a good coverage of climate stations and a high percentage of cropped area. On average, the model coefficient of determination at CAR level was 66%, 51% and 67%, for spring wheat, barley and canola, respectively. Skilful forecasts (i.e. model efficiency index>0) were achieved in 70% of the CARs for spring wheat and canola, and 43% for barley (low values observed in CAR with small harvested area). At the provincial scale, the mean absolute percentage errors (MAPE) of the September forecasts ranged from 7% to 16%, 7% to14%, and 6% to 14% for spring wheat, barley and canola, respectively. For forecasts at the national scale, MAPE values (i.e. 8%, 5% and 9% for the three respective crops) were considerably smaller than the corresponding historical coefficients of variation (i.e. 17%, 10% and 17% for the three crops). Overall, the ICCYF performed better for spring wheat than for canola and barley at all the three spatial scales. Skilful forecasts were achieved by mid-August, giving a lead time of about 1 month before harvest and about 3–4 months before the final release of official survey results. As such, the ICCYF could be used as a complementary tool for the traditional survey method, especially in areas where it is not practical to conduct such surveys

Four major circulation patterns, associated with daily precipitation in Portugal, are classified from daily sea level pressure fields over the northeastern Atlantic and western Europe, based on the K-means clustering algorithm coupled with principal component analysis. A rainy pattern is clearly identified with a probability of rain of 74.6%, as well as two distinct dry patterns, one prevailing in summer and the other occurring frequently in winter; a blocking-like pattern with a probability of rain of 36.8% has also been identified. These patterns are quasi-stationary, normally persisting for 1 week and sometimes even for 1 month, especially the dry ones; they represent the principal weather regimes associated with precipitation in Portugal. Interannual variations in monthly precipitation associated with the circulation patterns are also investigated; results show that these variations match fluctuations in the frequencies of occurrence of both the rainy and the dry patterns. The decreasing trend of March monthly rainfall in southern Portugal is closely related to corresponding trends in the frequencies of both the rainy pattern and the summer dry pattern. Long term trends are not significant either in other monthly rainfall sequences or in the frequencies of different circulation patterns. Interannual variations seem, in most months, to be quasi-periodic. Singular spectrum analysis (SSA) is performed on these sequences to detect quasi-periodic oscillations. Relationships between oscillations in rainfall and in frequencies of occurrence of circulation patterns are studied. Results show that four weather circulation patterns or weather regimes are important for investigating regional climate change in Portugal and its relationship with variability of large-scale atmospheric circulation. © 1998 Royal Meteorological Society