Hydropower is a key part of the increasing shift in power production from nonrenewables to renewable energy. In regions such as Africa, hydropower reservoirs are vital for achieving several sustainable development goals, including clean water, energy, and poverty elimination. However, the operations of hydropower reservoirs are often suboptimal due to the lack of hydrologic data for generating reliable inflow forecasts. Here, we present a decision support system (DSS) framework for hydropower planning at daily to seasonal time scales by combining data from earth observation satellites (EOS) with ensemble climate forecasts from dynamical models and hydrologic modeling. The large uncertainty inherent in satellite-based datasets is overcome by using a data validation framework which does not require ground-based measurements. In addition, an EOS evapotranspiration product is used as a proxy for streamflow in calibrating hydrologic models. Compared to a DSS forced with a climatological forecast (zero-skill), the hydropower production with the new DSS increased by 20%. The study highlights the advantage of using data from EOS in overcoming the issue of data scarcity in water resources applications, particularly in developing regions of the world such as Africa.

Abstract Accurate rainfall forecasts on timescales ranging from a few hours to several weeks are needed for many hydrological applications. This study examines bias, skill and reliability of four ensemble forecast systems (from Canada, UK, Europe, and the United States) and a multi‐model ensemble as applied to Ethiopian catchments. By verifying these forecasts on hydrological catchments, we focus on spatial scales that are relevant to many actual water forecasting applications, such as flood forecasting and reservoir optimization. By most verification metrics tested, the bias corrected European model is the best individual model at predicting daily rainfall variations, while the Canadian model shows the most realistic ensemble spread and thus the most reliable forecast probabilities, including those of extreme events. The skill of the multi‐model ensemble outperforms individual models by most metrics, and is skillful up to 9 days ahead. Skill is higher for the 0–5 day model accumulation than for the first 24 h, suggesting that timing errors strongly penalize the skill of forecasts with shorter accumulation periods. Due to seasonality in the model biases, bias correction is best applied to each month individually. Forecasting extreme rainfall is a challenge for Ethiopia, especially over mountainous regions where positive skill is only reached after bias correction. Compared to individual models, the multi‐model ensemble has a higher probability of detecting extreme rainfall and a lower false alarm rate, with usable skill at 24 h lead times.

ABSTRACTEurasian snow cover fraction (SCF) prediction is analyzed using the recently developed National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) monthly retrospective forecasts for 1983–2009. The CFSv2 is generally capable of reproducing the observed Eurasian SCF seasonal cycle and climatology. This study focuses on the prediction skill and predictability of Eurasian SCF in snowmelt and snowfall seasons because the intensive variability occurs in the two seasons. The CFSv2 reasonably predicts the interannual variations, long‐term trend and leading pattern in snowmelt season several months ahead. In comparison with the snowmelt season, the CFSv2 shows a better prediction skill in climatological values but a worse skill in the interannual variability in snowfall season. In addition, the forecasted downward trend of SCF in the snowfall season is opposite to that in the observation. The biases of Eurasian SCF in the snowmelt and snowfall seasons are significantly related with those of temperature and precipitation in the CFSv2. The forecasted cooler and wetter atmosphere is suggestive of the overestimation of the mean SCF. Meanwhile, the underestimation in the variability of both temperature and precipitation in the CFSv2 may be the important factor for the underestimated variability of SCF, especially for the damped variability of SCF in the snowfall season. Generally, the CFSv2 shows a higher and more stable prediction skill after late‐1990s than before in the two seasons. The change in the initial condition in the CFSv2 and the observed SCF in late‐1990s might be the plausible reason for it.

Projections of historical and future changes in climate extremes are examined by applying the bias-correction spatial disaggregation (BCSD) statistical downscaling method to five general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). For this analysis, 11 extreme temperature and precipitation indices that are relevant across multiple disciplines (e.g., agriculture and conservation) are chosen. Over the historical period, the simulated means, variances, and cumulative distribution functions (CDFs) of each of the 11 indices are first compared with observations, and the performance of the downscaling method is quantitatively evaluated. For the future period, the ensemble average of the five GCM simulations points to more warm extremes, fewer cold extremes, and more precipitation extremes with greater intensities under all three scenarios. The changes are larger under higher emissions scenarios. The inter-GCM uncertainties and changes in probability distributions are also assessed. Changes in the probability distributions indicate an increase in both the number and interannual variability of future climate extreme events. The potential deficiencies of the method in projecting future extremes are also discussed.

Observational studies and modeling experiments illustrate that variability in October Eurasian snow cover extent impacts boreal wintertime conditions over the Northern Hemisphere (NH) through a dynamical pathway involving the stratosphere and changes in the surface-based Arctic Oscillation (AO). In this paper, we conduct a comprehensive study of the Eurasian snow–AO relationship in twenty coupled climate models run under pre-industrial conditions from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Our analyses indicate that the coupled climate models, individually and collectively, do not capture well the observed snow–AO relationship. The models lack a robust lagged response between October Eurasian snow cover and several NH wintertime variables (e.g., vertically propagating waves and geopotential heights). Additionally, the CMIP5 models do not simulate the observed spatial distribution and statistics of boreal fall snow cover across the NH including Eurasia. However, when analyzing individual 40-year time slices of the models, there are periods of time in select models when the observed snow–AO relationship emerges. This finding suggests that internal variability may play a significant role in the observed relationship. Further analysis demonstrates that the models poorly capture the downward propagation of stratospheric anomalies into the troposphere, a key facet of NH wintertime climate variability irrespective of the influence of Eurasian snow cover. A weak downward propagation signal may be related to several factors including too few stratospheric vortex disruptions and weaker-than-observed tropospheric wave driving. The analyses presented can be used as a roadmap for model evaluations in future studies involving NH wintertime climate variability, including those considering future climate change.

Previous work has shown that the combined influence of El Nino-Southern Oscillation (ENSO) and the Madden-Julian oscillation (MJO) significantly impacts the wintertime circulation over North America for lead times up to at least 4 weeks. These findings suggest that both the MJO and ENSO may prove beneficial for generating a seamless prediction link between short-range deterministic forecasts and longer-range seasonal forecasts. To test the feasibility of this link, wintertime (December-March) probabilistic 2-m temperature (T2m) forecasts over North America are generated solely on the basis of the linear trend and statistical relationships with the initial state of the MJO and ENSO. Overall, such forecasts exhibit substantial skill for some regions and some initial states of the MJO and ENSO out to a lead time of approximately 4 weeks. In addition, the primary ENSO T2m regions of influence are nearly orthogonal to those of the MJO, which suggests that the MJO and ENSO generally excite different patterns within the continuum of large-scale atmospheric teleconnections. The strong forecast skill scores for some regions and initial states confirm the promise that information from the MJO and ENSO may offer forecasts of opportunity in weeks 3 and 4, which extend beyond the current 2-week extended-range outlooks of the National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Center (CPC), and an intraseasonal link to longer-range probabilistic forecasts.

Lagged ensembles from the operational Climate Forecast System version 2 (CFSv2) seasonal hindcast dataset are used to assess skill in forecasting interannual variability of the December–February Arctic Oscillation (AO). We find that a small but statistically significant portion of the interannual variance (>20 %) of the wintertime AO can be predicted at leads up to 2 months using lagged ensemble averages. As far as we are aware, this is the first study to demonstrate that an operational model has discernible skill in predicting AO variability on seasonal timescales. We find that the CFS forecast skill is slightly higher when a weighted ensemble is used that rewards forecast runs with the most accurate representations of October Eurasian snow cover extent (SCE), hinting that a stratospheric pathway linking October Eurasian SCE with the AO may be responsible for the model skill. However, further analysis reveals that the CFS is unable to capture many important aspects of this stratospheric mechanism. Model deficiencies identified include: (1) the CFS significantly underestimates the observed variance in October Eurasian SCE, (2) the CFS fails to translate surface pressure anomalies associated with SCE anomalies into vertically propagating waves, and (3) stratospheric AO patterns in the CFS fail to propagate downward through the tropopause to the surface. Thus, alternate boundary forcings are likely contributing to model skill. Improving model deficiencies identified in this study may lead to even more skillful predictions of wintertime AO variability in future versions of the CFS.

Recent studies have shown that the Madden–Julian Oscillation (MJO) impacts the leading modes of intraseasonal variability in the northern hemisphere extratropics, providing a possible source of predictive skill over North America at intraseasonal timescales. We find that a k-means cluster analysis of mid-level geopotential height anomalies over the North American region identifies several wintertime cluster patterns whose probabilities are strongly modulated during and after MJO events, particularly during certain phases of the El Niño-Southern Oscillation (ENSO). We use a simple new optimization method for determining the number of clusters, k, and show that it results in a set of clusters which are robust to changes in the domain or time period examined. Several of the resulting cluster patterns resemble linear combinations of the Arctic Oscillation (AO) and the Pacific/North American (PNA) teleconnection pattern, but show even stronger responses to the MJO and ENSO than clusters based on the AO and PNA alone. A cluster resembling the positive (negative) PNA has elevated probabilities approximately 8–14 days following phase 6 (phase 3) of the MJO, while a negative AO-like cluster has elevated probabilities 10–20 days following phase 7 of the MJO. The observed relationships are relatively well reproduced in the 11-year daily reforecast dataset from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). This study statistically links MJO activity in the tropics to common intraseasonal circulation anomalies over the North American sector, establishing a framework that may be useful for improving extended range forecasts over this region.

Correspondence to: Nat Johnson, International Pacific Research Center (IPRC), University of Hawaii at Manoa, Honolulu, HI 96822; E-mail: natj@hawaii.edu. Toward a Framework for Incorporating MJO and ENSO Information into CPC Probabilistic Extended Range Forecasts Nat Johnson, Emily Riddle, Marshall Stoner, Steven Feldstein, Dan Collins, Michelle L’Heureux IPRC, University of Hawaii at Manoa 2 Wyle Information Systems, Climate Prediction Center, NCEP/NWS/NOAA 2 Climate Prediction Center, NCEP/NWS/NOAA 4 Pennsylvania State University

Two statistical indices are developed to represent the northward progression of rainfall between late January and late July over eastern Africa. The first, a Seasonal Location Index, extracts the seasonal phase associated with a daily rainfall map using a principal component analysis. The second, a Rainfall Cluster Index, describes the seasonal cycle as a progression through a series of rainfall patterns that have been defined using a cluster analysis. The indices are used to identify large seasonal shifts in the latitude of rainfall, to examine when these shifts occur, and to investigate circulation features associated with perturbations ahead of and behind the mean seasonal cycle. These indices are potentially useful for exploring the predictability of rainfall transitions at both short-range and seasonal timescales. Copyright (C) 2012 Royal Meteorological Society

We examine the yearly occurrence of a monsoon jump of approximately 20° latitude during the boreal spring and summer rainy seasons over the Greater Horn of Africa (GHA). This jump is in contrast with a simple model of a smoothly varying ITCZ over the region. The rainfall jump is observed annually during April and May in three precipitation data sets and in regional climate model simulations using the PSU/NCAR Mesoscale Model (MM5). The MM5 simulations show the rainfall jump to be roughly coincident with abrupt circulation changes that occur as the Somali jet develops during April, May, and June. In particular, the cross‐equatorial (meridional) branch of the Somali jet forms along the East African coast in April, bringing moisture (and rainfall) northward to the southern slopes of the Ethiopian plateau. This meridional branch forms well before the northern zonal branch of the jet, which diverts moisture eastward from southern Ethiopia and feeds the Indian monsoon. These results establish a framework for understanding the precipitation cycle over the GHA, and provide a foundation for improving subseasonal forecasts over drought‐prone regions of eastern Africa.

During the summer of 2004, five altitude‐controlled tracking balloons were flown as part of the International Consortium for Atmospheric Research on Transport and Transformations (ICARTT) campaign. These Controlled Meteorological (CMET) balloons, newly developed at the University of Massachusetts, are notable for their light weight (∼1 kg mass), efficient altitude control, ease of launch, long‐duration flight capability, and ability to perform repeated quasi‐Lagrangian soundings. The balloons were embedded in urban plumes from New York and Boston which they tracked over New England, eastern Canada, and the Atlantic Ocean while maintaining a nearly constant altitude. The flights ranged from 10 to 111 hours and covered a maximum distance of 3000 km. Balloon flight tracks are used here to assess the accuracy of trajectory models during intensive aircraft sampling periods. A new method is presented for increasing the number of available reference trajectories by dividing the balloon flights into shorter segments for statistical analysis. For trajectory durations between 2 and 12 hours, mean trajectory errors are found to be approximately 26% and 34% of the flight distance for ECMWF‐based and GFS‐based trajectories, respectively. Anomalously large model errors observed during three of the flights are found to be the result of a narrow low‐level jet (15 July) and synoptic‐scale flow patterns (9 and 10 August). The results from this study should be useful to researchers evaluating the performance of trajectory models and chemical transport models during the ICARTT campaign. Complete CMET balloon and model trajectory data sets are available as a supplement to this paper.