In this study, changes in the spatial and temporal patterns of climate extreme indices were analyzed. Daily maximum and minimum air temperature, precipitation, and their association with climate change were used as the basis for tracking changes at 50 meteorological stations in Iran over the period 1975–2010. Sixteen indices of extreme temperature and 11 indices of extreme precipitation, which have been quality controlled and tested for homogeneity and missing data, are examined. Temperature extremes show a warming trend, with a large proportion of stations having statistically significant trends for all temperature indices. Over the last 15 years (1995–2010), the annual frequency of warm days and nights has increased by 12 and 14 days/decade, respectively. The number of cold days and nights has decreased by 4 and 3 days/decade, respectively. The annual mean maximum and minimum temperatures averaged across Iran both increased by 0.031 and 0.059 °C/decade. The probability of cold nights has gradually decreased from more than 20 % in 1975–1986 to less than 15 % in 1999–2010, whereas the mean frequency of warm days has increased abruptly between the first 12-year period (1975–1986) and the recent 12-year period (1999–2010) from 18 to 40 %, respectively. There are no systematic regional trends over the study period in total precipitation or in the frequency and duration of extreme precipitation events. Statistically significant trends in extreme precipitation events are observed at less than 15 % of all weather stations, with no spatially coherent pattern of change, whereas statistically significant changes in extreme temperature events have occurred at more than 85 % of all weather stations, forming strongly coherent spatial patterns.

Particularly in regions, where precipitation is limited to a few months per year only, reliable determination of the onset of the rainy season and the start of the sowing time is of crucial importance to sustainable food production. Especially since the mid-1980s, an increasing delay of onset dates in the Volta basin of West Africa has been suspected by local farmers. To investigate this speculation and develop a reliable tool to find the optimal sowing date, the onset of the rainy season in the region was analysed by means of several statistical techniques. The focus was put on the region of the Volta basin in Ghana and Burkina Faso. In a first step, two fuzzy logic based definitions of the onset were developed using daily precipitation data and additionally accounting for important plant physiological aspects. In this context, only one definition is potentially useful to judge whether the current onset of the rainy season has already begun. In a second step, methods for predicting the onset date of the ongoing season were investigated. In this context, the detection of onset controlling variables plays a major role. Two strategies are investigated and evaluated for the prediction of the monsoon's onset dates: 1) A combination of regionalized synoptic rainfall data by means of principal component analysis (PCA) in a spatial mode and linear discriminant analysis in order to detect reliable prediction parameters and allow for a classification of the rainy season, dry season, and the onset of the rainy season using current rainfall data. 2) Linear regression models were generated to estimate the onset of the rainy season for certain regions using the onset dates of regions, where the onset has already begun. To enhance the predictability, optimized definition parameterisation in the field of both strategies was applied. Copyright © 2007 Royal Meteorological Society

This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continuous time series. As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation. According to the empirical copulas, significant upper and lower tail dependence between observed and modelled precipitation can be observed. These dependence structures are further conditioned on the prevailing large-scale weather situation. Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.

The high intra-seasonal rainfall variability and the lack of adaptive capacities are the major limiting factors for rainfed agricultural production in smallholder farming systems across Sub-Saharan Africa. Therefore, the crop planting date, a low-cost agricultural management strategy aiming to alleviate crop water stress can contribute to enhance agricultural decision-making, particularly as a climate change adaptation strategy. By considering the crop water requirements throughout the crop growing cycle using a process-based crop model in conjunction with a fuzzy rule-based planting date approach, location-specific planting rules were derived for maize cropping in Burkina Faso (BF). Then, they were applied to regional future climate projections to derive optimized planting dates (OPDs) for the 2020s (2011–2030) and the 2040s (2031–2050), respectively. Based on potential maize yield simulations driven by climate change projections and planting dates, the OPD approach was compared with a well-established planting date method for West Africa and evaluated as a potential adaptation strategy for climate change. On average, the OPD approach achieved approximately +15% higher potential maize yield regardless of the regional climate model (RCM) and the period. However, the potential yield surpluses strongly decreased from the North to the South. Regarding climate change adaptation, the combined impact of climate change and the OPD approach has shown on average, a mean maize yield deviation between −23% and 34% in comparison to the 1989–2008 baseline period. Yield deviation is found to depend strongly on the RCM and location. The RCM ensemble mean yield for the period 2011–2050 revealed a maximum decrease of 8% compared to the baseline period. On the one hand, these findings highlight the potential of the OPDs as a crop management strategy but, on the other hand, it is apparent that farmers need to combine the OPDs with others suited farming practices to adequately respond to climate change

For an improved understanding of the hydrometeorological conditions of the Tana River basin of Kenya, East Africa, its joint atmospheric-terrestrial water balances are investigated. This is achieved through the application of the Weather Research and Forecasting (WRF) and the fully coupled WRF-Hydro modeling system over the Mathioya-Sagana subcatchment (3279 km²) and its surroundings in the upper Tana River basin for 4 years (2011–2014). The model setup consists of an outer domain at 25 km (East Africa) and an inner one at 5-km (Mathioya-Sagana subcatchment) horizontal resolution. The WRF-Hydro inner domain is enhanced with hydrological routing at 500-m horizontal resolution. The results from the fully coupled modeling system are compared to those of the WRF-only model. The coupled WRF-Hydro slightly reduces precipitation, evapotranspiration, and the soil water storage but increases runoff. The total precipitation from March to May and October to December for WRF-only (974 mm/year) and coupled WRF-Hydro (940 mm/year) is closer to that derived from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data (989 mm/year) than from the TRMM (795 mm/year) precipitation product. The coupled WRF-Hydro-accumulated discharge (323 mm/year) is close to that observed (333 mm/year). However, the coupled WRF-Hydro underestimates the observed peak flows registering low but acceptable NSE (0.02) and RSR (0.99) at daily time step. The precipitation recycling and efficiency measures between WRF-only and coupled WRF-Hydro are very close and small. This suggests that most of precipitation in the region comes from moisture advection from the outside of the analysis domain, indicating a minor impact of potential land-precipitation feedback mechanisms in this case. The coupled WRF-Hydro nonetheless serves as a tool in quantifying the atmospheric-terrestrial water balance in this region.