Uncertainty In Future Projections Research Articles

Uncertainties in Future Projections of Summer Droughts and Heat Waves over the Contiguous United States

Droughts and heat waves have important impacts on multiple sectors including water resources, agriculture, electricity generation, and public health, so it is important to understand how they will be affected by climate change. However, there is large uncertainty in the projected changes of these extreme events from climate models. In this study, historical biases in models are compared against their future projections to understand and attempt to constrain these uncertainties. Biases in precipitation, near-surface air temperature, evapotranspiration, and a land–atmospheric coupling metric are calculated for 24 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) against 2 models from phase 2 of the North American Land Data Assimilation System (NLDAS-2) as reference for 1979–2005. These biases are highly correlated across variables, with some models being hotter and drier and others wetter and cooler. Models that overestimate summer precipitation project larger increases in precipitation, evapotranspiration, and land–atmospheric coupling over important agricultural regions by the end of the twenty-first century (2070–99) under RCP8.5, although the percentage variance explained is low. Changes in the characteristics of droughts and heat waves are calculated and linked to historical biases in precipitation and temperature. A method to constrain uncertainty by ranking models based on historical performance is discussed but the rankings differ widely depending on the variable considered. Despite the large uncertainty that remains in the magnitude of the changes, there is consensus among models that droughts and heat waves will increase in multiple regions in the United States by the end of the twenty-first century unless climate mitigation actions are taken.

Estimating trends in the global mean temperature record

Abstract. Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to de-emphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate, and time series methods that account for internal variability in nonparametric rather than parametric ways. However, given a limited data record and the presence of internal variability, estimating radiatively forced temperature trends in the historical record necessarily requires some assumptions. Ostensibly empirical methods can also involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to be applicable, but long enough for long-timescale internal variability to be accounted for. In the context of global mean temperatures, empirical methods that appear to de-emphasize assumptions can therefore produce misleading inferences, because the trend over the twentieth century is complex and the scale of temporal correlation is long relative to the length of the data record. We illustrate here how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can allow for a wider array of questions to be addressed. In particular, the model allows one to distinguish, within a single statistical framework, between uncertainties in the shorter-term vs. longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric statistical methods, if attuned to the important characteristics of internal variability, can result in more accurate uncertainty statements about trends.

One possible uncertainty in CMIP5 projections of low‐oxygen water volume in the Eastern Tropical Pacific

AbstractUsing the results from nine Earth system models submitted to the Coupled Model Intercomparison Project Phase 5 (CMIP5), we identify the Eastern Tropical Pacific (ETP) as the region with the greatest uncertainty of future changes in oxygen‐deficient (<30 μM) water volumes since different models variously project both positive and negative changes in the oxygen‐deficient volume and export flux there. We investigate the factors controlling future changes in oxygen‐deficient volume in the ETP with global warming using a single off‐line biogeochemical model. Oxygen budget analysis clarifies that the Equatorial Undercurrent (EUC) is the key mechanism controlling future variations in the oxygen‐deficient volume in the ETP in our model. From the outputs of all of the CMIP5 models and our model, we identify a significant negative relationship between changes in the EUC volume transport and the oxygen‐deficient water volume from the present to the end of the 21st century, which indicates that the response of the EUC to global warming leads to one possible uncertainty in future projections of oxygen‐deficient volume in the ETP.

Assessing urban water security under changing climate: Challenges and ways forward

Climate change has altered the elements of water availability, water demand and climate extremes in time and space. Such changes have various implications on water security, particularly in urban regions that are highly concentrated with human population and socio-economic activities. Assessing the climate change impacts on water security is often based on using the projections from an ensemble of climate models which are further downscaled into finer spatial resolutions. The availability of downscaled climate simulations has resulted in the optimism that future climate change threats to urban water security could now be quantified at the scale of individual cities; however, is this really the case? To showcase some of the challenges in the direct application of downscaled climate projections for impact assessment, here we explore the reliability of a state-of-the-art ensemble of downscaled climate simulations in the city of Montreal, Canada. We show that spatial variability in long-term climate over Montreal is misrepresented by downscaled climate projections. In addition, uncertainty in future projections as a result of climate models and/or concentration pathways can pose extra challenges in application of the downscaled projection in real-world design and operational contexts. Based on the currently available literature, we suggest few directions to handle current modeling uncertainties until improved climate modeling technology becomes available.

Timeslice experiments for understanding regional climate projections: applications to the tropical hydrological cycle and European winter circulation

A set of atmosphere-only timeslice experiments are described, designed to examine the processes that cause regional climate change and inter-model uncertainty in coupled climate model responses to \(CO_2\) forcing. The timeslice experiments are able to reproduce the pattern of regional climate change in the coupled models, and are applied here to two cases where inter-model uncertainty in future projections is large: the tropical hydrological cycle, and European winter circulation. In tropical forest regions, the plant physiological effect is the largest cause of hydrological cycle change in the two models that represent this process. This suggests that the CMIP5 ensemble mean may be underestimating the magnitude of water cycle change in these regions, due to the inclusion of models without the plant effect. SST pattern change is the dominant cause of precipitation and circulation change over the tropical oceans, and also appears to contribute to inter-model uncertainty in precipitation change over tropical land regions. Over Europe and the North Atlantic, uniform SST increases drive a poleward shift of the storm-track. However this does not consistently translate into an overall polewards storm-track shift, due to large circulation responses to SST pattern change, which varies across the models. Coupled model SST biases influence regional rainfall projections in regions such as the Maritime Continent, and so projections in these regions should be treated with caution.

On the frequency of the 2015 monsoon season drought in the Indo‐Gangetic Plain

AbstractThe 2015 drought in the Indo‐Gangetic Plain posed new challenges related to food and water security and affected the lives of millions. All‐India monsoon rainfall in 2015 was the tenth driest year on record (1906–2015) with a deficit of 14.5%, and the Indo‐Gangetic Plain witnessed a rainfall deficit of 25.8% (third rank event). Drought severity was amplified by deficits from the previous years in the Indo‐Gangetic Plain and other parts of India. The Indo‐Gangetic Plain faced a 2 year cumulative deficit of 51%, and the drought of 2014–2015 was unprecedented with a return period of 542 years. The GRACE data showed the occurrence of consecutive negative terrestrial water storage and groundwater anomalies in 2014 and 2015, mainly centered over the Indo‐Gangetic Plain region. Notwithstanding uncertainty in future projections, the multiyear droughts in the same regions can pose challenges for water resources and agriculture.

Towards improved and more routine Earth system model evaluation in CMIP

Abstract. The Coupled Model Intercomparison Project (CMIP) has successfully provided the climate community with a rich collection of simulation output from Earth system models (ESMs) that can be used to understand past climate changes and make projections and uncertainty estimates of the future. Confidence in ESMs can be gained because the models are based on physical principles and reproduce many important aspects of observed climate. More research is required to identify the processes that are most responsible for systematic biases and the magnitude and uncertainty of future projections so that more relevant performance tests can be developed. At the same time, there are many aspects of ESM evaluation that are well established and considered an essential part of systematic evaluation but have been implemented ad hoc with little community coordination. Given the diversity and complexity of ESM analysis, we argue that the CMIP community has reached a critical juncture at which many baseline aspects of model evaluation need to be performed much more efficiently and consistently. Here, we provide a perspective and viewpoint on how a more systematic, open, and rapid performance assessment of the large and diverse number of models that will participate in current and future phases of CMIP can be achieved, and announce our intention to implement such a system for CMIP6. Accomplishing this could also free up valuable resources as many scientists are frequently "re-inventing the wheel" by re-writing analysis routines for well-established analysis methods. A more systematic approach for the community would be to develop and apply evaluation tools that are based on the latest scientific knowledge and observational reference, are well suited for routine use, and provide a wide range of diagnostics and performance metrics that comprehensively characterize model behaviour as soon as the output is published to the Earth System Grid Federation (ESGF). The CMIP infrastructure enforces data standards and conventions for model output and documentation accessible via the ESGF, additionally publishing observations (obs4MIPs) and reanalyses (ana4MIPs) for model intercomparison projects using the same data structure and organization as the ESM output. This largely facilitates routine evaluation of the ESMs, but to be able to process the data automatically alongside the ESGF, the infrastructure needs to be extended with processing capabilities at the ESGF data nodes where the evaluation tools can be executed on a routine basis. Efforts are already underway to develop community-based evaluation tools, and we encourage experts to provide additional diagnostic codes that would enhance this capability for CMIP. At the same time, we encourage the community to contribute observations and reanalyses for model evaluation to the obs4MIPs and ana4MIPs archives. The intention is to produce through the ESGF a widely accepted quasi-operational evaluation framework for CMIP6 that would routinely execute a series of standardized evaluation tasks. Over time, as this capability matures, we expect to produce an increasingly systematic characterization of models which, compared with early phases of CMIP, will more quickly and openly identify the strengths and weaknesses of the simulations. This will also reveal whether long-standing model errors remain evident in newer models and will assist modelling groups in improving their models. This framework will be designed to readily incorporate updates, including new observations and additional diagnostics and metrics as they become available from the research community.

Sources of Uncertainty in Future Projections of the Carbon Cycle

Abstract The inclusion of carbon cycle processes within CMIP5 Earth system models provides the opportunity to explore the relative importance of differences in scenario and climate model representation to future land and ocean carbon fluxes. A two-way analysis of variance (ANOVA) approach was used to quantify the variability owing to differences between scenarios and between climate models at different lead times. For global ocean carbon fluxes, the variance attributed to differences between representative concentration pathway scenarios exceeds the variance attributed to differences between climate models by around 2025, completely dominating by 2100. This contrasts with global land carbon fluxes, where the variance attributed to differences between climate models continues to dominate beyond 2100. This suggests that modeled processes that determine ocean fluxes are currently better constrained than those of land fluxes; thus, one can be more confident in linking different future socioeconomic pathways to consequences of ocean carbon uptake than for land carbon uptake. The contribution of internal variance is negligible for ocean fluxes and small for land fluxes, indicating that there is little dependence on the initial conditions. The apparent agreement in atmosphere–ocean carbon fluxes, globally, masks strong climate model differences at a regional level. The North Atlantic and Southern Ocean are key regions, where differences in modeled processes represent an important source of variability in projected regional fluxes.

Construction of rainfall change scenarios over the Chilka Lagoon in India

The present study attempted to quantify long-term seasonal and annual rainfall change for the period 1901–2004 over the Chilka Lagoon in India, the second largest lagoon in the world using multiple gridded data sources. The future rainfall projection is also constructed using the four Representative Concentration Pathways (RCPs) of the Global Circulation Models (GCMs) of the Coupled Model Intercomparison Project phase 5 (CMIP5). Skill of GCMs to simulate the observed rainfall over the lagoon was investigated through estimation of long-term trends and comparison of mean seasonal cycles using Taylor diagram. Finally based on the combined results obtained through trend analysis as well as seasonal cycles, 12 better performing GCMs were selected. Ensemble mean of better performing GCMs reveal that the rainfall in annual, monsoon and winter seasons have increased in the last century similar to three observational gridded data sources. The projected seasonal cycle of rainfall from different RCPs shows a dipole like characteristics where the drier (winter) and moist (monsoon) seasons show a surplus of rainfall (11–25%) while the premonsoon and the postmonsoon seasons show a deficient rainfall (3–52%) at the end of 21st century. It is interesting to note that the Chilka Lake will expected to receive an increasing amount of annual rainfall by 3–7% in 2020s, 7–11% in 2050s and 10–21% in 2080s. Ensemble mean of future downscaled scenarios revealed that the annual rainfall will increase slightly higher rate as compared to without downscaling indicating high uncertainty in future projection.

Uncertainty in twenty-first century projections of the Atlantic Meridional Overturning Circulation in CMIP3 and CMIP5 models

Uncertainty in the strength of the Atlantic Meridional Overturning Circulation (AMOC) is analyzed in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) projections for the twenty-first century; and the different sources of uncertainty (scenario, internal and model) are quantified. Although the uncertainty in future projections of the AMOC index at 30°N is larger in CMIP5 than in CMIP3, the signal-to-noise ratio is comparable during the second half of the century and even larger in CMIP5 during the first half. This is due to a stronger AMOC reduction in CMIP5. At lead times longer than a few decades, model uncertainty dominates uncertainty in future projections of AMOC strength in both the CMIP3 and CMIP5 model ensembles. Internal variability significantly contributes only during the first few decades, while scenario uncertainty is relatively small at all lead times. Model uncertainty in future changes in AMOC strength arises mostly from uncertainty in density, as uncertainty arising from wind stress (Ekman transport) is negligible. Finally, the uncertainty in changes in the density originates mostly from the simulation of salinity, rather than temperature. High-latitude freshwater flux and the subpolar gyre projections were also analyzed, because these quantities are thought to play an important role for the future AMOC changes. The freshwater input in high latitudes is projected to increase and the subpolar gyre is projected to weaken. Both the freshening and the gyre weakening likely influence the AMOC by causing anomalous salinity advection into the regions of deep water formation. While the high model uncertainty in both parameters may explain the uncertainty in the AMOC projection, deeper insight into the mechanisms for AMOC is required to reach a more quantitative conclusion.

Changes in precipitation over the La Plata Basin, projected by CLARIS-LPB regional models

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 68:169-182 (2016) - DOI: https://doi.org/10.3354/cr01388 Contribution to the CR Special: 'CLARIS-La Plata Basin: climate variability and uncertainties' Changes in precipitation over the La Plata Basin, projected by CLARIS-LPB regional models Iracema Fonseca de Albuquerque Cavalcanti*, Virginia Piccinini Silveira Center of Weather Forecasting and Climate Studies/National Institute for Space Research (CPTEC/INPE), Rodovia Presidente Dutra, Km 40, CEP: 12630-000, Cachoeira Paulista, SP, Brazil *Corresponding author: iracema.cavalcanti@cptec.inpe.br ABSTRACT: The La Plata Basin (LPB) is an important region for social and economic sectors of South America, mainly as a source of water and for agriculture. Regional models are used to analyze changes in precipitation over South America in order to provide more details of the projected changes in specific regions. Results of regional models from the CLARIS-LPB project are analyzed to assess changes and uncertainties in future projections for the LPB compared to the base period. Results from several models are taken into account in order to assess uncertainties. The models agree in projecting more precipitation in the whole basin and in an increased frequency of extreme wet austral summers. The majority of models agree in projections of drier conditions in the upper LPB and an increase in the frequency of extreme dry austral winters and springs. However, in the lower LPB the projections indicate an increase in the frequency of extreme wet winters and springs. In austral autumn the uncertainties are high in the upper LPB. The uncertainties are low regarding increases in the frequency of rainy days in the middle and lower LPB and in the maximum number of consecutive dry days in the upper LPB. The projected patterns of austral summer anomaly precipitation variability obtained from empirical orthogonal function analysis display the same dipole between southern LPB and the South Atlantic Convergence Zone as in the base period, but increased anomalies in the northern center. In the austral winter the only anomaly signal simulated in the base period remains in the future projections, but the maximum variability is displaced southwards. KEY WORDS: Precipitation extremes · Regional models · La Plata Basin · Climate change · Frequency of extremes · Precipitation variability · Precipitation indices Full text in pdf format PreviousNextCite this article as: Cavalcanti IFA, Silveira VP (2016) Changes in precipitation over the La Plata Basin, projected by CLARIS-LPB regional models. Clim Res 68:169-182. https://doi.org/10.3354/cr01388 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 68, No. 2-3. Online publication date: May 04, 2016 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2016 Inter-Research.

Intensification of the Western North Pacific Anticyclone Response to the Short Decaying El Niño Event due to Greenhouse Warming

Abstract Two types of El Niño evolution have been identified in terms of the lengths of their decaying phases: the first type is a short decaying El Niño that terminates in the following summer after the mature phase, and the second type is a long decaying one that persists until the subsequent winter. The responses of the western North Pacific anticyclone (WNPAC) anomaly to the two types of evolution are remarkably different. Using experiments from phase 5 of the Coupled Model Intercomparison Project (CMIP5), this study investigates how well climate models reproduce the two types of El Niño evolution and their impacts on the WNPAC in the historical period (1950–2005) and how they will change in the future under anthropogenic global warming. To reduce uncertainty in future projection, the nine best models are selected based on their performance in simulating El Niño evolution. In the historical run, the nine best models’ multimodel ensemble (B9MME) well reproduces the enhanced (weakened) WNPAC that is associated with the short (long) decaying El Niño. The comparison between results of the historical run for 1950–2005 and the representative concentration pathway 4.5 run for 2050–99 reveals that individual models and the B9MME tend to project no significant changes in the two types of El Niño evolution for the latter half of the twenty-first century. However, the WNPAC response to the short decaying El Niño is considerably intensified, being associated with the enhanced negative precipitation anomaly response over the equatorial central Pacific. This enhancement is attributable to the robust increase in mean and interannual variability of precipitation over the equatorial central Pacific under global warming.

Uncertainty in future projections of the North Pacific subtropical high and its implication for California winter precipitation change

AbstractThis study examines future projections of sea level pressure change in the North Pacific and its impact on winter precipitation changes in California. The multimodel analysis, based on the Coupled Model Intercomparison Project phase 5 models under the Representative Concentration Pathway 8.5 scenario, shows a robust sea level pressure change in the late 21st century over the western North Pacific in which both the Aleutian Low and the North Pacific subtropical high (NPSH) shift poleward in concert with a widening of the Hadley cell. This change is partly explained by a systematic increase of static stability in the subtropics. Despite its robustness, the projected NPSH changes over the eastern North Pacific exhibit a substantial intermodel spread, contributing as a cause for uncertain projections of precipitation changes in California. This intermodel spread in the eastern North Pacific is associated with a Pacific Decadal Oscillation‐like surface temperature change in the western North Pacific and the resulting meridional temperature gradient change. This study points to a major source of uncertainty for the response of winter precipitation to global warming over the West Coast of North America: atmosphere‐ocean coupling in the North Pacific.

Selecting regional climate scenarios for impact modelling studies

In climate change research ensembles of climate simulations are produced in an attempt to cover the uncertainty in future projections. Many climate change impact studies face difficulties using the full number of simulations available, and therefore often only subsets are used. Until now such subsets were chosen based on their representation of temperature change or by accessibility of the simulations. By using more specific information about the needs of the impact study as guidance for the clustering of simulations, the subset fits the purpose of climate change impact research more appropriately. Here, the sensitivity of such a procedure is explored, particularly with regard to the use of different climate variables, seasons, and regions in Europe. While temperature dominates the clustering, the resulting selection is influenced by all variables, leading to the conclusion that different subsets fit different impact studies best.

Understanding Uncertainties in Future Projections of Seasonal Tropical Precipitation

Abstract Projected changes in regional seasonal precipitation due to climate change are highly uncertain, with model disagreement on even the sign of change in many regions. Using a 20-member CMIP5 ensemble under the RCP8.5 scenario, the intermodel uncertainty of the spatial patterns of projected end-of-twenty-first-century change in precipitation is found not to be strongly influenced by uncertainty in global mean temperature change. In the tropics, both the ensemble mean and intermodel uncertainty of regional precipitation change are found to be predominantly related to spatial shifts in convection and convergence, associated with processes such as sea surface temperature (SST) pattern change and land–sea thermal contrast change. The authors hypothesize that the zonal-mean seasonal migration of these shifts is driven by 1) the nonlinear spatial response of convection to SST changes and 2) a general movement of convection from land to ocean in response to SST increases. Assessment of tropical precipitation model projections over East Africa highlights the complexity of regional rainfall changes. Thermodynamically driven moisture increases determine the magnitude of the long rains (March–May) ensemble mean precipitation change in this region, whereas model uncertainty in spatial shifts of convection accounts for almost all of the intermodel uncertainty. Moderate correlations are found across models between the long rains precipitation change and patterns of SST change in the Pacific and Indian Oceans. Further analysis of the capability of models to represent present-day SST–rainfall links, and any relationship with model projections, may contribute to constraining the uncertainty in projected East Africa long rains precipitation.

Comparison of RCM and GCM projections of boreal summer precipitation over Africa

AbstractTo provide input data for the potential future climate change impact assessment at regional levels, dynamic downscaling of global climate models (GCMs) climate using regional climate models (RCMs) is frequently utilized. It is important to understand how the climate change signal is modified in the RCM driven by GCM so that the simulated results can be accurately interpreted. This study compares the performance of a RCM and four driving GCMs in simulating precipitation over Africa and investigate how dynamically downscaled present and future climate from the regional model RegCM4.1 driven with four GCMs differ from those of the GCMs. In general, RegCM4.1 has a lower summer (June–August) precipitation bias than the driving GCMs, increasing our confidence in their future projections. Despite uncertainty in future projections, RegCM4.1 shows decreased precipitation over Sahel regardless of which GCM is used to drive the RCM. RegCM4.1 shows an increased interannual variability of summer precipitation over Gulf of Guinea and Central Africa as compared to GCMs in both present‐day climate and projected future climate scenarios. The mean annual cycle of precipitation differs between RCM and the driving GCM, in particular during the West African Monsoon (WAM). The WAM onset, peak, and retreat phases are more clearly defined in the RegCM4.1 than the four driving GCMs, where the WAM rainband shifts more southward. This paper highlights the uncertainties in dynamical downscaling for climate prediction, which are due to the inconsistencies in the physical packages between RCM and GCMs.

Climate and water availability indicators in Canada: Challenges and a way forward. Part III – Future scenarios

Following from Parts I and II of this series (this issue), some common approaches for developing and assessing future scenarios of water availability are reviewed, along with recent case studies of Canadian watersheds. The results of future changes in drought related to climate change are influenced by the choice of indicators. For the Standardized Precipitation Index (SPI), small changes in drought frequency and severity are projected over southern Canada, reflecting the influence of increases in future annual precipitation only. However, assessments using the Palmer Drought Severity Index (PDSI) reveal dramatic increases in the potential for future droughts since this indicator incorporates the combined influences of higher temperatures, soil moisture capacity and precipitation to estimate evapotranspiration. Regarding projected changes in runoff, watersheds in British Columbia tend to show increases in annual and winter runoff. Some watersheds show projected decreases in summer runoff. In the southern Prairies, most watersheds show projected decreases in annual and summer runoff. In Ontario and Quebec, results are mixed. Lake levels in the Great Lakes are projected to decline under most scenarios, but results differ between regional and global climate model-based scenarios due to differences in how lake evaporation is calculated. In New Brunswick, Labrador and northern Quebec, streamflow is projected to increase. Uncertainties in future projections emerge due to differences between climate scenario generating methods, and between hydrologic models used for the assessments. This paper concludes with some thoughts on addressing important research questions related to future scenarios of water availability in Canada. For scenario-based assessments, hydrologic model inter-comparisons might yield some useful insights into uncertainties in model structure that affect evaporation, evapotranspiration and snowmelt calculations. Scenarios developed for assessments should include both future climate and projected land use/cover changes and, where necessary, integration of potential reduction in glacier volume. Finally, as scenarios from regional climate models become more readily available, there may be more opportunity to explore how runoff projections could be applied to basin-scale routing models.

How large are uncertainties in future projection of reference evapotranspiration through different approaches?

• Analyze the uncertainties in ET 0 projection from estimation method and data quality. • Provide reliable future projection of ET 0 from GCMs. • Discuss the implication of future ET 0 change to regional water resources. As the indicator of atmospheric evaporative demand over a hypothetical reference surface, reference evapotranspiration (ET 0 ) is an important input to hydrological models. Future projections of ET 0 are of great importance in assessing the potential impacts of climate change on the hydrologic regime as well as water resources systems. Different estimating formulations and different input data reliabilities existing in practice determine there may be potential uncertainty in projection of future ET 0 change. Here we investigated the difference of future ET 0 response to climate change based on three approaches, i.e., more physically based Penman–Montieth equation with relatively uncertain downscaled data quality, more empirical temperature-based Hargreaves equation with more reliable downscaled input data, and statistical downscaling method with directly selecting ET 0 as predictands. The Hanjiang River Basin, a headwater source of famous South to North Water Diversion Project (SNWDP) in China was chosen as example to illustrate this issue. Although similar increase processes of ET 0 in the Hanjiang River Basin were suggested by three methods, the magnitude of ET 0 increase differs substantially, indicating that uncertainty still exist despite of approximate performance of these methods in simulating general trends. Whilst increasing aridity index and decreasing water surplus over the period of 2011–2099 would inevitably cause negative impacts on the implementation of the SNWDP and effective adapting measures are thus expected.

Modelling the Climatic Variability in the Niger Delta Region: Influence of Climate Change on Hydrology

With substantial variations to flood occurrence predicted as a result of climate change it becomes significant to investigate how global hydrological models process climate-forcing data. The paper presents reasonable analyses of projected hydrological effects of climate change on the Niger Delta region, Nigeria. Runoff simulated represent the present and future flood risk for the catchment of the River Niger using Global Hydrological Models (GHMs) from EU WATCH project driven by three GCMs. The simulated discharges are compared with the monthly gauge measurement along the River Niger from the Global Runoff Data Centre (GRDC). A period of (1970-2050) was chosen to understand the climatic variability across the Niger Delta region. The GHMs under Special Report on Emissions Scenarios A2 scenario were used to provide future climate scenarios over the Niger River. The hydrological models from EU WATCH project were used to calculate flood extents for different model outputs. The simulation shows clears trends of increased in river discharge over the catchment although uncertainty cannot be over role. Considering the future climate suggests that river flow from the basin could be substantially increased, especially in the long term when compare to the reference period. It is not applicable to formulate precise recommendations for management of runoff in the Niger Delta region as Todd highlighted that the degree of uncertainty in future projections of river flow is consider as a constrained towards developing applicable adaptation activities. The rationale behind this work is the need to understand in clear terms the climate change threats on the Niger Delta region; this will form a practical basis for developing adaptation strategies to manage future climate risks.

On the generation of climate model ensembles

Climate model ensembles are used to estimate uncertainty in future projections, typically by interpreting the ensemble distribution for a particular variable probabilistically. There are, however, different ways to produce climate model ensembles that yield different results, and therefore different probabilities for a future change in a variable. Perhaps equally importantly, there are different approaches to interpreting the ensemble distribution that lead to different conclusions. Here we use a reduced-resolution climate system model to compare three common ways to generate ensembles: initial conditions perturbation, physical parameter perturbation, and structural changes. Despite these three approaches conceptually representing very different categories of uncertainty within a modelling system, when comparing simulations to observations of surface air temperature they can be very difficult to separate. Using the twentieth century CMIP5 ensemble for comparison, we show that initial conditions ensembles, in theory representing internal variability, significantly underestimate observed variance. Structural ensembles, perhaps less surprisingly, exhibit over-dispersion in simulated variance. We argue that future climate model ensembles may need to include parameter or structural perturbation members in addition to perturbed initial conditions members to ensure that they sample uncertainty due to internal variability more completely. We note that where ensembles are over- or under-dispersive, such as for the CMIP5 ensemble, estimates of uncertainty need to be treated with care.