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There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning. [ABSTRACT FROM AUTHOR]

The article focuses on the U.S. National Aeronautics and Space Administration (NASA's) Precipitation Measurement Missions (PMM). NASA determines the success and relevance of its missions by understanding and quantifying how the data are applied within different communities of end users, including national weather and hydrology agencies.

The Madden-Julian Oscillation (MJO) is the dominant mode of sub-seasonal climate variability in the global tropics. As such it represents an opportunity for intra-seasonal rainfall prediction and, perhaps, for explaining dynamics that underlie longer term variability and trends. This opportunity is of substantial interest for tropical Africa, where rainfall variability has significant impacts on agriculture, energy, and natural ecosystems. The objective of this review paper is to inventory and assess the state of knowledge of MJO influence on African rainfall. A number of studies have identified statistical links between MJO and sub-seasonal rainfall variability in West, East, and Southern Africa. The proposed mechanisms to explain this influence differ by region and by season, and they often involve multiscale interactions between local precipitation processes and MJO-associated atmospheric dynamics. Dynamically-based forecast systems have some skill in predicting MJO evolution to time horizons of 3–4 weeks, and some can capture teleconnections to Africa. On longer time scales, there is evidence that MJO activity both modulates and is modulated by the El Niño Southern Oscillation and the Indian Ocean Dipole. The implications of these interactions for MJO connections to Africa require further research, as does the potential for trends in MJO behavior and impacts on Africa under global climate change. [ABSTRACT FROM AUTHOR]

Many studies have documented dramatic climatic and environmental changes that have affected Africa over different time scales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from observations and proxies and/or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, applications of hierarchical climate regionalizations of Africa using observations and GCM historical simulations and future projections are presented. First, Africa is regionalized based on interannual precipitation variability using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data for the period 1981-2014. A number of data processing techniques and clustering algorithms are tested to ensure a robust definition of climate regions. These regionalization results highlight the seasonal and even month-to-month specificity of regional climate associations across the continent, emphasizing the need to consider time of year as well as research question when defining a coherent region for climate analysis. CHIRPS regions are then compared to those of five GCMs for the historic period, with a focus on boreal summer. Results show that some GCMs capture the climatic coherence of the Sahel and associated teleconnections in a manner that is similar to observations, while other models break the Sahel into uncorrelated subregions or produce a Sahel-like region of variability that is spatially displaced from observations. Finally, shifts in climate regions under projected twenty-first-century climate change for different GCMs and emissions pathways are examined. A projected change is found in the coherence of the Sahel, in which the western and eastern Sahel become distinct regions with different teleconnections. This pattern is most pronounced in high-emissions scenarios. [ABSTRACT FROM AUTHOR]

Ranking the performance of global climate models (GCMs) is a notoriously difficult exercise. Multi-model comparison studies nearly always show that each model has strengths and weaknesses relative to others, and for many purposes the multi-model ensemble mean delivers better estimates than any individual model. Nevertheless, in regions like East Africa, where there is little consensus between models on the magnitude or sign of 21st century precipitation change, the multi-model ensemble mean approach to climate projection provides little value for adaptation planning. Here, we consider several possible frameworks for model evaluation and ranking, and assess the differences in performance of a subset of models participating in the 5th Coupled Model Intercomparison Project (CMIP5) according to each framework. Our test case is precipitation in the Nile River headwaters regions. We find that there is little consistency in the relative performance of models across frameworks based on amount and seasonality of precipitation, interannual precipitation variability, precipitation teleconnections, and continental scale climate patterns. These analyses offer some guidance on which GCMs are most likely to provide meaningful results for specific applications, but they caution that any effort to select 'best performing' GCMs for the Nile River basin must carefully consider the purposes for which GCMs are being selected. [ABSTRACT FROM AUTHOR]

This study examines daily precipitation data during December-March over south equatorial Africa (SEA) and proposes a new zonal asymmetric pattern (ZAP) that explains the leading mode of weather-scale precipitation variability in the region. The eastern and western components of the ZAP, separated at about 30°E, appear to be a consequence of an anomalous zonal atmospheric cell triggered by enhanced low-level westerly winds. The enhanced westerlies are generated by a diagonal interhemispheric pressure gradient between the southwestern Indian and north tropical Atlantic Oceans. In eastern SEA these winds hit the East African Plateau, producing low-level convergence and convection that further intensifies the westerlies. In western SEA a subsiding branch develops in response, closing the circulation cell. The system gradually dissipates as the pressure gradient weakens. Through this mechanism, simultaneous changes in two hemispheres generate a regional zonally oriented circulation that relies on climatic communication between eastern and western equatorial Africa. [ABSTRACT FROM AUTHOR]

Advanced land surface models (LSMs) offer detailed estimates of distributed hydrological fluxes and storages. These estimates are extremely valuable for studies of climate and water resources, but they are difficult to verify as field measurements of soil moisture, evapotranspiration, and surface and subsurface runoff are sparse in most regions. In contrast, river discharge is a hydrologic flux that is recorded regularly and with good accuracy for many of the world's major rivers. These measurements of discharge spatially integrate all upstream hydrological processes. As such, they can be used to evaluate distributed LSMs, but only if the simulated runoff is properly routed through the river basins. In this study, a rapid, computationally efficient source-to-sink (STS) routing scheme is presented that generates estimates of river discharge at gauge locations based on gridded runoff output. We applied the scheme as a postprocessor to archived output of the Global Land Data Assimilation System (GLDAS). GLDAS integrates satellite and ground-based data within multiple offline LSMs to produce fields of land surface states and fluxes. The application of the STS routing scheme allows for evaluation of GLDAS products in regions that lack distributed in situ hydrological measurements. We found that the four LSMs included in GLDAS yield very different estimates of river discharge and that there are distinct geographic patterns in the accuracy of each model as evaluated against gauged discharge. The choice of atmospheric forcing data set also had a significant influence on the accuracy of simulated discharge. [ABSTRACT FROM AUTHOR]

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equivalent—and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring. [ABSTRACT FROM AUTHOR]

The Euphrates Plain (EP) experiences large interannual variability in vegetation cover, especially in areas of marginal rain-fed agriculture. Vegetation in this region is primarily limited by available soil moisture, as determined by winter precipitation, spring precipitation, and air temperature. Satellite analyses indicate that the springtime normalized difference vegetation index (NDVI) is negatively correlated with surface albedo, and that interannual variability in albedo in the EP produces an estimated forcing on the radiation balance that peaks at 16.0 W m-2 in May. Simulations with a regional climate model indicate that surface energy fluxes during a drought year (1999) differed substantially from those during a year with normal precipitation (2003). These differences were geographically specific, with the EP exhibiting increased albedo and decreased sensible heat flux while the neighboring Zagros Plateau region showed no albedo effect, a large increase in sensible heat flux, and an offsetting reduction in latent heat flux. In both the EP and the Zagros there was a potential for positive feedbacks on temperature and drought in late spring, though the most likely feedback mechanisms differed between the two regions: in the EP surface brightening leads to cooling and reduced turbulent heat flux, while in the Zagros region reduced latent heat flux leads to warming and a deepening of the planetary boundary layer. [ABSTRACT FROM AUTHOR]

The association between elevation (agro-climatic zones, ACZs) and the mean annual total rainfall (MATRF) is not straightforward in different parts of the world. This study sought to estimate the amount of MATRF across four elevation zones of Jema watershed, which is situated in the northwestern highlands of Ethiopia, by employing an appropriate interpolation method. The elevation of the watershed ranges from 1895 to 3518 m a.s.l. For the sake of this study, 34 sample MATRF data were extracted from satellite and nearby gauge stations that were recorded from 1983 to 2010. These data sources were reconstructed by International Research Institute for Climate and Society at Columbia University, USA, at a scale of 10 km by 10 km. An elevation data set generated from a digital elevation model with 30-m resolution (DEM 30 m) was considered as a covariable to estimate the MATRF. To identify the optimal interpolation model, mean errors were computed using cross-validation statistics. The root-mean-square error (RMSE) analysis showed that ordinary cokriging (OCK) was the most accurate model with a predictive power of 87.3%. The root-mean-square standardized (RMSSE) analysis showed that the best precision value (0.72) occurred in OCK. Stable and Gaussian trend lines together with local polynomial types of trend removal, and an elliptical neighborhood search function could perform best to maximize the accuracy and the precision of estimating MATRF. Elevation, as a covariable, enhanced the degree of accuracy and precision of estimation. The value of the trend line function (least square) between the MATRF and elevation was very weak (R2 = 0.07), whereas the value of trend line function (least square) between the MATRF and the longitude coordinates (east–west direction) was medium (R2 = 0.34). The estimated MATRF for the entire watershed under study ranged from 1228 to 1640 mm. To conclude, elevation could contribute to the estimation of the MATRF. The value of the MATRF showed a declining pattern from the lower to higher elevation areas of the watershed. [ABSTRACT FROM AUTHOR]

With support from NASA's Modeling and Analysis Program, we have recently developed the NASA Unified-Weather Research and Forecasting model (NU-WRF). NU-WRF is an observation-driven integrated modeling system that represents aerosol, cloud, precipitation and land processes at satellite-resolved scales. “Satellite-resolved” scales (roughly 1–25 km), bridge the continuum between local (microscale), regional (mesoscale) and global (synoptic) processes. NU-WRF is a superset of the National Center for Atmospheric Research (NCAR) Advanced Research WRF (ARW) dynamical core model, achieved by fully integrating the GSFC Land Information System (LIS, already coupled to WRF), the WRF/Chem enabled version of the GOddard Chemistry Aerosols Radiation Transport (GOCART) model, the Goddard Satellite Data Simulation Unit (G-SDSU), and custom boundary/initial condition preprocessors into a single software release, with source code available by agreement with NASA/GSFC. Full coupling between aerosol, cloud, precipitation and land processes is critical for predicting local and regional water and energy cycles. [ABSTRACT FROM AUTHOR]

• Our precipitation isotope data shows the need for high-frequency spatial sampling. • Daily δ18O precip values decrease by >8‰ synchronously at 3 stations in August 2013. • The northwestern highland stations show similar seasonal trends in δ18O precip. • Seasonality of δ18O precip at Jijiga is less distinct than in northwest highlands. • Storm intensity and convection likely explain seasonal isotope variations. Stable isotopes of oxygen (δ18O) and deuterium (δ2H) in precipitation are used as tracers of the hydrologic cycle in studies of both past and present climate. In Ethiopia, a drought sensitive region where direct observations of climate are limited, understanding the stable isotopic composition of precipitation can provide an integrated view of the hydroclimate today and help interpret records of climate variability in the past. To date, the isotopic composition of precipitation in Ethiopia is known primarily from monthly precipitation collections from one long-term monitoring station in Addis Ababa. Here we report δ18O and δ2H values of precipitation from a 16-month long sampling campaign at four stations in Ethiopia that represent different climate regimes. Precipitation samples collected at daily, weekly and monthly intervals between August 2012 and November 2013 at these stations exhibit a large range of δ18O (−12.6 to +10.3‰), δ2H (−91.8‰ to +80.8‰), and d-excess values (−9.9‰ to +29.9‰). The four stations have similar amount-weighted mean δ18O values, but exhibit different seasonal patterns in δ18O values as clearly shown in daily and weekly data. The most striking feature of this dataset is the synchronous decrease in δ18O values in daily and weekly precipitation samples from the three stations in the northwestern highlands (Gondar, Debre Markos, Jimma) during the first three weeks of August 2013. This decrease in δ18O values does not correlate strongly to precipitation amount but it is likely due to a combination of increased storm intensity and deep convection at the time of lower δ18O values of precipitation. Our high-resolution dataset provides new insights into the controls on the isotopic composition of rainfall in Ethiopia and how we might use it to understand hydroclimate variability today and in the past. [ABSTRACT FROM AUTHOR]