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The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from El Niño to Yangtze River basin rainfall meant that the strong El Niño in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements. [ABSTRACT FROM AUTHOR]

Summer precipitation anomalies over eastern China are characterized spatially by meridionally banded structures fluctuating on interannual and interdecadal timescales, leading to regional droughts and floods. In addition to long-term trends, how these patterns may change under global warming has important implications for agricultural planning and water resources over this densely populated area. Using the latest Hadley Centre climate model, HadGEM3-GC2, this paper investigates the potential response of summer precipitation patterns over this region, by comparing the leading modes between a 4×CO2 simulation and the model’s pre-industrial control simulation. Empirical Orthogonal Function (EOF) analyses show that the first two leading modes account for about 20% of summer rainfall variability. EOF1 is a monopole mode associated with the developing phase of ENSO events and EOF2 is a dipole mode associated with the decaying phase of ENSO. Under 4×CO2 forcing, the dipole mode with a south-north orientation becomes dominant because of a strengthened influence from excessive warming of the Indian Ocean. On interdecadal time scales, the first EOF looks very different from the control simulation, showing a dipole mode of east-west contrast with enhanced influence from high latitudes. [ABSTRACT FROM AUTHOR]

In this paper, we present regional climate change projections for the Tamil Nadu state of India, simulated by the Met Office Hadley Centre regional climate model. The model is run at 25 km horizontal resolution driven by lateral boundary conditions generated by a perturbed physical ensemble of 17 simulations produced by a version of Hadley Centre coupled climate model, known as HadCM3Q under A1B scenario. The large scale features of these 17 simulations were evaluated for the target region to choose lateral boundary conditions from six members that represent a range of climate variations over the study region. The regional climate, known as PRECIS, was then run 130 years from 1970. The analyses primarily focus on maximum and minimum temperatures and rainfall over the region. For the Tamil Nadu as a whole, the projections of maximum temperature show an increase of 1.0, 2.2 and 3.1 °C for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095), respectively, with respect to baseline period (1970-2000). Similarly, the projections of minimum temperature show an increase of 1.1, 2.4 and 3.5 °C, respectively. This increasing trend is statistically significant (Mann-Kendall trend test). The annual rainfall projections for the same periods indicate a general decrease in rainfall of about 2-7, 1-4 and 4-9 %, respectively. However, significant exceptions are noticed over some pockets of western hilly areas and high rainfall areas where increases in rainfall are seen. There are also indications of increasing heavy rainfall events during the northeast monsoon season and a slight decrease during the southwest monsoon season. Such an approach of using climate models may maximize the utility of high-resolution climate change information for impact-adaptation-vulnerability assessments. [ABSTRACT FROM AUTHOR]

Precipitation variability imposes significant pressure in areas where agricultural practice is dominated by smallholder farmers who are dependent on subsistence farming. Advances in the understanding of this variability, in both time and space, have an important role to play in increasing the resilience of agricultural systems. The need is particularly pressing in regions of the world such as the African continent, which is already affected by multiple stresses including poverty and economic and political instability. In this paper, we explore the use of generalised linear models (GLMs) for this purpose, via a case study from north-east South Africa. A GLM is used to link the local rainfall variability to large-scale climate drivers identified from previous subcontinental-scale analyses, and the ability of the resulting model to simulate precipitation features that are relevant in agricultural applications is evaluated. We focus in particular on a set of growing season indices, proposed for the investigation of intraseasonal characteristics relevant for maize production in the region. Seven indices were computed from spatially averaged daily rainfall series from nine stations in the study area. As a first attempt to use GLMs for this type of application, the results are encouraging and suggest that the models are able to reproduce a range of agriculture-relevant indices. However, further research into spatial correlation structure is recommended to improve the multisite generation of the rainfall-derived characteristics. [ABSTRACT FROM AUTHOR]

A revised 25-point Shuman-Shapiro Spatial Filter (RSSSF) has been applied to six atmospheric circulation models and multi-model ensemble (MME) predictions, and its effect on the improvement of model forecast skill scores of the Asian summer precipitation anomaly is discussed in this paper. On the basis of 21-yr model ensemble predictions, the RSSSF can remove the unpredictable ‘noise’ with respect to the 2-grid wavelength in the model precipitation anomaly fields and maintain the large-scale counterpart, which is related to the response of the model to large-scale boundary forcing. Therefore, this could possibly enhance the forecast skill of the Asian summer rainfall anomaly in the models and the MME. The potential improvement of model forecasting skill is found in the Asian summer monsoon region, where the anomaly correlation coefficient (ACC) has been improved by 7–40%, corresponding to the decreased root mean square error (RMSE) in the model and the MME precipitation anomaly forecasts. [ABSTRACT FROM AUTHOR]

In a globally warming world, subtropical regions are generally expected to become drier while the tropics and mid-high latitudes become wetter. In line with this, Southwest China, close to 25°N, is expected to become increasingly prone to drought if annual mean precipitation decreases. However, despite this trend, changes in the temporal distribution of moisture supply might actually result in increased extreme rainfall in the region, whose climate is characterized by distinct dry and wet seasons. Using hourly and daily gauge observations, rainfall intensity changes since 1971 are examined for a network of 142 locations in the region. From the analysis, dry season changes are negligible but wet season changes exhibit a significantly strong downward trend [−2.4% (10 yr)−1], particularly during the past 15 years [−17.7% (10 yr)−1]. However, the intensity of events during the wettest of 5% hours appears to steadily increase during the whole period [1.4% (10 yr)−1], tying in with government statistical reports of recent droughts and flooding. If the opposing trends are a consequence of a warming climate, it is reasonable to expect the contradictory trend to continue with an enhanced risk of flash flooding in coming decades in the region concerned. [ABSTRACT FROM AUTHOR]

The global model analysis and forecast have a significant impact on the regional model predictions, as global model provides the initial and lateral boundary condition to regional model. This study addresses an important question whether the regional model can improve the short-range weather forecast as compared to the global model. The National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) and the Weather Research and Forecasting (WRF) model are used in this study to evaluate the performance of global and regional models over the Indian region. A 24-h temperature and specific humidity forecast from the NCEP GFS model show less error compared to WRF model forecast. Rainfall prediction is improved over the Indian landmass when WRF model is used for rainfall forecast. Moreover, the results showed that high-resolution global model analysis (GFS4) improved the regional model forecast as compared to low-resolution global model analysis (GFS3). [ABSTRACT FROM AUTHOR]

In this study, the performance of dynamical seasonal forecast systems is evaluated for the prediction of short rain anomalies over equatorial East Africa. The evaluation is based on observational datasets and the Asia-Pacific Climate Center (APCC) Ocean-Atmosphere coupled multi-model ensemble (MME) retrospective forecasts (hindcasts). These forecast systems have different hindcast periods; here, we have selected common years from 1982 to 2005. The ensembles of individual models and their MME mean are evaluated. Hindcasts initialized on the 1st of August from each year alone are considered, as these are the most relevant to short rain predictions. The coupled climate model ensemble reproduces the spatial distribution of mean September-October-November (SON) rainfall and seasonal climate variations over equatorial East Africa with further improvement in MME mean. Individual coupled models and MME mean also show statistically significant skill in forecasting sea surface temperatures anomalies (SSTAs) over the western and eastern parts of the equatorial Indian Ocean, giving significant correlation at 99 % confidence level for Indian Ocean dipole (IOD). Moreover, five out of ten coupled models and MME mean show statistically significant skill in predicting equatorial East Africa short rains. The fidelity of hindcasts is further measured by anomaly correlation coefficient (ACC) and four models as well as MME mean show significant skill over East Africa. It is shown that the reproduction of the observed variability in the East African region is mainly due to a realistic relationship of East African rainfall with the Indian Ocean dipole. Overall, the skill of the dynamical models is attributed to the fact that slowly evolving SSTs are the primary source of predictability and to the fact that coupled climate models produce skillful predictions of SON SST anomalies over the tropical Indian Ocean. This information opens the possibility of using readily available seasonal forecasts as skillful predictions of equatorial East Africa short rains. [ABSTRACT FROM AUTHOR]

Precipitation extremes affect various economic sectors and may result in substantial costs for societies. Future projections of such extreme occurrences are needed to successfully develop robust regional adaptation strategies. Model ensemble-based approaches provide a higher level of confidence since they compensate to some degree for the uncertainties of individual climate model projections. An ensemble of twelve regional climate projections from five regional climate models was used to evaluate the suitability of a modified version of the Rainfall Anomaly Index (mRAI) as an alternative to the Standardised Precipitation Index (SPI) in assessing future precipitation conditions. We compared frequency distributions and trends of the mRAI with the SPI for a test region that is climatologically representative of Central Eastern Europe. Both indices are highly correlated with each other at all tested timescales-both for stations and for regionally averaged data-with Pearson correlation coefficients >>0.9 and Spearman correlation coefficients >0.99. There are no significant differences in their frequency distributions, although the mRAI shows slightly higher frequencies in the classes of 'moderately dry' to 'very dry' conditions. The change signals revealed by SPI and mRAI are very similar for mean changes as well as for changes in the extremes. Considering the large bandwidth of change signals of individual regional climate projections, the mRAI provides sufficiently robust results for the evaluation of future precipitation anomaly trends. The notably more complex calculation of the SPI has no appreciable advantage for this application. [ABSTRACT FROM AUTHOR]

This research was aimed at addressing aspects related to variation in the amount of precipitation during the period from 1986 to 2011 in the Paraná River Hydrographical Basin, Brazil, for 32 meteorological stations using 11 climate indices created by the ETCCDI (Expert Team, ET, on Climate Change Detection and Indices, ETCCDI). The daily rainfall data were organized in spreadsheets, which were subjected to an intense quality control and an accurate historical research. For each pluviometric index, we have estimated the trends and the statistical significant of the slopes have been calculated. The results confirm that an increase in total precipitation in almost all analyzed stations was registered, and the extreme precipitations were the main contributors to such additions. In fact, the significant increase in total annual rainfall in north-central sector of the basin are related to higher rates of heavy rain, mainly above 95th percentile, as well as to the highest event of rainfall above 10 mm. Instead the northern part of the region, showed declining trends of extreme rainfall, caused mainly by the reduction in the rainfall occurrences over 95th percentile. In order to evaluate the impact that the increasing extreme rainfall may cause in large urban centers, we have investigated the data of two municipalities (Curitiba, PR and Goiânia, GO-Brazil), where the positive trend can cause inconvenience to the population (floods and inundations) suggesting, at least, the need of implementation of more effective urban planning for the future. [ABSTRACT FROM AUTHOR]

The interpretations of trend behaviour for dry and wet events are analysed in order to verify the dryness and wetness episodes. The fitting distribution of rainfall is computed to classify the dry and wet events by applying the standardised precipitation index (SPI). The rainfall amount for each station is categorised into seven categories, namely extremely wet, severely wet, moderately wet, near normal, moderately dry, severely dry and extremely dry. The computation of the SPI is based on the monsoon periods, which include the northeast monsoon, southwest monsoon and inter-monsoon. The trends of the dry and wet periods were then detected using the Mann-Kendall trend test and the results indicate that the major parts of Peninsular Malaysia are characterised by increasing droughts rather than wet events. The annual trends of drought and wet events of the randomly selected stations from each region also yield similar results. Hence, the northwest and southwest regions are predicted to have a higher probability of drought occurrence during a dry event and not much rain during the wet event. The east and west regions, on the other hand, are going through a significant upward trend that implies lower rainfall during the drought episodes and heavy rainfall during the wet events. [ABSTRACT FROM AUTHOR]

The influence of local black carbon (BC) on monthly mean precipitation and the associated circulation in South Asia is investigated. The results show that the amplified shortwave radiative heating rate by BC absorption enhances convective activity near the Himalayas and the southern Tibetan Plateau with increased rainfall anomalies in April and May, which is consistent with previous studies. However, the enhanced vertical motions and the precipitation ultimately lead towards cooling of the lower troposphere especially in May, which is extended till June. This favored negative rainfall responses in June, implying delay in the onset of South Asian summer monsoon rainfalls over Arabian Sea and western parts of the subcontinent. The negative precipitation response is further associated with the prevailing anomalous high pressure and the anticyclonic wind circulations induced by BC. Thus, we present here one different feature associated with BC-induced elevated heat pump-like circulations in South Asia. [ABSTRACT FROM AUTHOR]