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The spatial variability of turbulence above a forest has been examined. Two measurement towers were erected 800 m apart within a heterogeneous mixed forest located in the north east of the Netherlands. The measurements of u * /u were analysed and subsequently used to test a surface layer model. The model simulated the magnitude of the measurements reasonably well, but measured trends were not always reproduced by the model. The variable (du/dz)/u did not adapt as quickly to the new surface as u * /u. This is in agreement with Schmid (1994), and can be explained by a local decrease in mixing length. It is recommended to adapt the mixing length near a surface transition to improve the accuracy of surface layer models of heterogeneous landscapes.

Global warming has brought great pressure on the environment and livelihood conditions in Sudan and South Sudan. It is desirable to analyze and predict the change of critical climatic variables, such as temperature and precipitation, which will provide valuable reference results for future water resources planning and management in the region. The aims of this study are to test the applicability of the Long Ashton Research Station Weather Generator (LARS-WG) model in downscaling daily precipitation and daily maximum (Tmax) and daily minimum (Tmin) temperatures in Sudan and South Sudan and use it to predict future changes of precipitation; Tmin and Tmax for nine stations in Sudan and South Sudan are based on the SRA2 scenario of seven General Circulation Models (GCMs) outputs for the periods of 2011–2030, 2046–2065, and 2080–2099. The results showed that (1) the LARS-WG model produces good performance in downscaling daily precipitation and excellent performance in downscaling Tmax and Tmin in the study region; (2) downscaled precipitation from the prediction of seven GCMs showed great inconsistency in these two regions, which illustrates the great uncertainty in GCMs' results in the regions; (3) predicted precipitation in rainy season JJA (June, July, and August) based on the ensemble mean of seven GCMs showed a decreasing trend in the periods of 2011–2030, 2046–2065, and 2080–2099 in Sudan; however, an increasing trend can be found in SON (September, October, and November) in the future; (4) precipitation in South Sudan has an increasing trend in most seasons in the future except in MAM (March, April, and May) season in 2011–2030; and (5) predictions from seven GCMs showed a similar and continuous increasing trend for Tmax and Tmin in all three future periods, which will bring severe negative influence on improving livelihoods and reducing poverty in Sudan and South Sudan.

The study presents a methodology to characterise short- or long-term drought events, designed to aid understanding of how climate change may affect future risk. An indicator of drought magnitude, combining parameters of duration, spatial extent and intensity, is presented based on the Standardised Precipitation Index (SPI). The SPI is applied to observed (1955–2003) and projected (2003–2050) precipitation data from the Community Integrated Assessment System (CIAS). Potential consequences of climate change on drought regimes in Australia, Brazil, China, Ethiopia, India, Spain, Portugal and the USA are quantified. Uncertainty is assessed by emulating a range of global circulation models to project climate change. Further uncertainty is addressed through the use of a high-emission scenario and a low-stabilisation scenario representing a stringent mitigation policy. Climate change was shown to have a larger effect on the duration and magnitude of long-term droughts, and Australia, Brazil, Spain, Portugal and the USA were highlighted as being particularly vulnerable to multi-year drought events, with the potential for drought magnitude to exceed historical experience. The study highlights the characteristics of drought which may be more sensitive under climate change. For example, on average, short-term droughts in the USA do not become more intense but are projected to increase in duration. Importantly, the stringent mitigation scenario had limited effect on drought regimes in the first half of the twenty-first century, showing that adaptation to drought risk will be vital in these regions.

The Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site has a rich history of actively sensed cloud observations. Fourteen years (1997–2010) of observations from the Millimeter Cloud Radar (MMCR), Micropulse Lidar (MPL), and Belfort/Vaisala Ceilometers are used to understand how instrument selection and sampling impacts estimates of Cloud Fraction (CF) at this location. Although all instruments should be used in combination for the best estimates of CF, instrument downtime limits available samples and increases observational errors, demanding that users make sacrifices when calculating CF at longer intervals relevant to climate studies. Selection of MMCR or MMCR + MPL cloud masks changes very little in the overall understanding of total CF. Addition of the MPL increases the 14-year average CF by 9 %, mainly through an increase in optically thin high clouds year-round, and mid-level clouds during the summer months. Splitting the period into two equal 7-year periods reveals negligible change in MMCR + MPL CF. For the MMCR, however, CF deceases by 6.1 %. This sudden change in CF occurs around the time the radar was upgraded, suggesting that this decrease is tied to hardware sensitivity or scanning strategy changes. Users must be cognizant of this and other issues when calculating CF from the variety of observations available at the ARM SGP site.

Dry spells are an essential concept of drought climatology that clearly defines the semiarid Mediterranean environment and whose consequences are a defining feature for an ecosystem, so vulnerable with regard to water. The present study was conducted to characterize rainfall drought in the Segura River basin located in eastern Spain, marked by the self seasonal nature of these latitudes. A daily precipitation set has been utilized for 29 weather stations during a period of 20 years (1993–2013). Furthermore, four sets of dry spell length (complete series, monthly maximum, seasonal maximum, and annual maximum) are used and simulated for all the weather stations with the following probability distribution functions: Burr, Dagum, error, generalized extreme value, generalized logistic, generalized Pareto, Gumbel Max, inverse Gaussian, Johnson SB, Log-Logistic, Log-Pearson 3, Triangular, Weibull, and Wakeby. Only the series of annual maximum spell offer a good adjustment for all the weather stations, thereby gaining the role of Wakeby as the best result, with a p value means of 0.9424 for the Kolmogorov-Smirnov test (0.2 significance level). Probability of dry spell duration for return periods of 2, 5, 10, and 25 years maps reveal the northeast-southeast gradient, increasing periods with annual rainfall of less than 0.1 mm in the eastern third of the basin, in the proximity of the Mediterranean slope.

High-resolution, daily precipitation climate products that realistically represent extremes are critical for evaluating local-scale climate impacts. A popular bias-correction method, empirical quantile mapping (EQM), can generally correct distributional discrepancies between simulated climate variables and observed data but can be highly sensitive to the choice of calibration period and is prone to overfitting. In this study, we propose a hybrid bias-correction method for precipitation, EQM-LIN, which combines the efficacy of EQM for correcting lower quantiles, with a robust linear correction for upper quantiles. We apply both EQM and EQM-LIN to historical daily precipitation data simulated by a regional climate model over a region in the northeastern USA. We validate our results using a five-fold cross-validation and quantify performance of EQM and EQM-LIN using skill score metrics and several climatological indices. As part of a high-resolution downscaling and bias-correction workflow, EQM-LIN significantly outperforms EQM in reducing mean, and especially extreme, daily distributional biases present in raw model output. EQM-LIN performed as good or better than EQM in terms of bias-correcting standard climatological indices (e.g., total annual rainfall, frequency of wet days, total annual extreme rainfall). In addition, our study shows that EQM-LIN is particularly resistant to overfitting at extreme tails and is much less sensitive to calibration data, both of which can reduce the uncertainty of bias-correction at extremes.

¶The dependence of the discharge (Q) of two contrasting UK rivers (Itchen, Ewe) on concurrent and lagged regional climate (RC) and atmospheric circulation (AC) variations was assessed over the period 1974–97. RC variables used were temperature and precipitation; the AC indicators used were 850 hPa water vapour flux anomalies (VF) at five western European stations, and the Arctic (AOI) and North Atlantic Oscillation (NAOI) indices. Correlation analyses were performed to assess Q-RC and Q-AC relationships before two sets of multiple linear regression models were developed to specify monthly Q values from RC and AC. Q-RC associations were generally stronger and more seasonally consistent than Q-AC relationships, with the flow of the Itchen (southern England) and Ewe (northern Scotland) being most sensitive to temperature (TEMP) and precipitation (PPT) respectively. In most months, discharge values of both rivers were positively associated to zonal and vector VF anomalies over the British Isles and northern France, but inversely related to vector VF over Iceland. The AOI and NAOI were significantly related to the Ewe’s flow only; relationships were strongest in the winter half-year. Monthly AC regression models explained 14.8–81.0% (25.0–90.9%) of the discharge variability of the Itchen (Ewe). Strong AC forcing of the Itchen’s discharge is confined to the winter (DJF), since the Itchen’s direct meteorological signal is attenuated by groundwater dynamics in other seasons. Analysis of anomalous flow periods (e.g. 1988–92 and 1995–7) revealed that discharge does not always respond in the same manner to a given RC/AC forcing, as the relationships themselves vary inter-annually as well as between the two rivers.

This paper evaluates the simulation performance of the 37 coupled models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) with respect to the East Asian summer monsoon (EASM) and the Indo-Pacific warm pool and North Pacific Ocean dipole (IPOD) and also the interrelationships between them. The results show that the majority of the models are unable to accurately simulate the interannual variability and long-term trends of the EASM, and their simulations of the temporal and spatial variations of the IPOD are also limited. Further analysis showed that the correlation coefficients between the simulated and observed EASM index (EASMI) is proportional to those between the simulated and observed IPOD index (IPODI); that is, if the models have skills to simulate one of them then they will likely generate good simulations of another. Based on the above relationship, this paper proposes a conditional multi-model ensemble method (CMME) that eliminates those models without capability to simulate the IPOD and EASM when calculating the multi-model ensemble (MME). The analysis shows that, compared with the MME, this CMME method can significantly improve the simulations of the spatial and temporal variations of both the IPOD and EASM as well as their interrelationship, suggesting the potential for the CMME approach to be used in place of the MME method.